Methylation markers for diagnosing hepatocellular carcinoma and lung cancer

ABSTRACT

Disclosed herein, in certain embodiments, are methods and kits for diagnosing a subject as having hepatocellular carcinoma (HCC) or lung cancer. In some instances, also described herein are methods of determining the prognosis of the subject having HCC or lung cancer. In additional instances, described herein are methods of determining the specific staging of HCC or lung cancer in a subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 15/910,857, filed on Mar. 2, 2018, now U.S. Pat. No. 10,513,739, issued on Dec. 24, 2019, which claims the benefit of U.S. Provisional Application No. 62/466,328, filed on Mar. 2, 2017, U.S. Provisional Application No. 62/466,329, filed on Mar. 2, 2017, and U.S. Provisional Application No. 62/569,462, filed on Oct. 6, 2017, each of which is incorporated herein by reference in its entirety.

SUBMISSION OF SEQUENCE LISTING ON ASCII TEXT FILE

The content of the following submission on ASCII text file is incorporated herein by reference in its entirety: a computer readable form (CRF) of the Sequence Listing (file name: 165182000801SEQLIST.TXT, date recorded: May 4, 2020, size: 10 KB).

BACKGROUND OF THE DISCLOSURE

Cancer is a leading cause of deaths worldwide, with annual cases expected to increase from 14 million in 2012 to 22 million during the next two decades (WHO). Diagnostic procedures for liver cancer, in some cases, begin only after a patient is already present with symptoms, leading to costly, invasive, and sometimes time-consuming procedures. In addition, inaccessible areas sometimes prevent an accurate diagnosis. Further, high cancer morbidities and mortalities are associated with late diagnosis.

SUMMARY OF THE DISCLOSURE

Disclosed herein, in certain embodiments, are methods and kits for diagnosing a subject as having hepatocellular carcinoma (HCC) or lung cancer. In some instances, also described herein are methods of determining the prognosis of the subject having HCC or lung cancer. In additional instances, described herein are methods of determining the specific staging of HCC or lung cancer in a subject.

In certain embodiments, disclosed herein is a method of selecting a subject suspected of having hepatocellular carcinoma (HCC) or lung cancer for treatment, comprising (a) contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from Tables 2, 6, 7, 9, or 10 to generate an amplified product, wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified product to generate a methylation profile of the gene; (c) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC or lung cancer; (d) evaluating an output from the model to determine whether the subject has HC or lung cancer; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC or lung cancer.

In certain embodiments, disclosed herein is a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or mote genes selected from SOCS2, EPSTI1, TIA1, Chromosome 4, Chromosome 6, ZNF323. FOXP4, and GRHL2 from the treated DNA; (c) obtaining a methylation score based on the methyls lion profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In certain embodiments, disclosed herein is a method of determining the prognosis of a subject having lung cancer or monitoring the progression of lung cancer in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from NPBWR1, Chromosome 2, AAK1, SIM1. C10orf46, C17orf101, DEPDC5, ZNF323, GABRA2, PLAC8, and ADRA2B from the treated DNA; (c) obtaining a methyl ation score based on tltc methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first, therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In certain embodiments, disclosed herein is a kit comprising a set of nucleic acid probes that hybridizes to target sequences of SOCS2, EPSTI1, TIA1, Chromosome 4, Chromosome 6, ZNF323, FOXP4, GRHL2, NPBWR1, Chromosome 2, AAK1, SIM1, C10orf46, C17orf101, DEPDC5, ZNF323, GABRA2, PLAC8, and ADRA2B.

In certain embodiments, disclosed herein is a kit comprising a set of nucleic acid probes that hybridizes to one or more genes selected from Tables 2, 6, 7, 9, or 10.

In certain embodiments, disclosed herein is a method of selecting a subject suspected of having hepatocellular carcinoma (HCC) for treatment, comprising: (a) contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATN1, Chromosome 6:170, Chromosome 6:3, ATAD2 anti Chromosome 8:20 to generate an amplified product, wherein the treated DNA is processed from a biological sample obtained front the subject; (b) analyzing the amplified product to generate a methylation profile of the gene; (c) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC; (d) evaluating an output from the model to determine whether the subject has HCC; and (c) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC.

In certain embodiments, disclosed herein is a method of selecting a subject suspected of having hepatocellular carcinoma (HCC) for treatment, comprising: (a) contacting treated DNA with a plurality of probes to generate amplified products, wherein each probe hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20, and each probe is different, and wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified products to generate a methylation profile of the genes from the gene panel; (e) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC: (d) evaluating an output from the model to determine whether the subject has HCC; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC.

In certain embodiments, disclosed herein is a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In certain embodiments, disclosed herein is a method of detecting the methylation status of one or more genes of a gene panel in a subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; and (b) detecting the methylation status m a gene selected from the gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20 by contacting the treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of the gene to generate an amplified product; and analyzing the amplified product to determine the methylation status of the gene.

In certain embodiments, disclosed herein is a method of detecting the methylation status of one or more genes of a gene panel in a subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; and (b) detecting the methylation status in a gene selected from the gene panel consisting of SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B by contacting the treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of the gene to generate an amplified product; and analyzing the amplified product to determine the methylation status in the gene.

In certain embodiments, disclosed herein is a kit comprising a set of nucleic acid probes that hybridizes to target sequences of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, Chromosome 8:20, SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of the disclosure are set forth with particularity in the appended claims. The file of this patent contains at least one drawing/photograph executed in color. Copies of this patent with color drawing(s)/photograph(s) will be provided by the Office upon request and payment of the necessary fee, A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

FIG. 1A-FIG. 1B illustrate exemplary workflows for building the diagnostic (FIG. 1A) and prognostic (FIG. 1B) models.

FIG. 2A-FIG. 2D illustrate cfDNA methylation analysis for diagnosis of LUNC and HCC.

FIG. 2A shows receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of the diagnostic prediction model (cd-score) using cfDNA methylation analysts in the validation cohort:

FIG. 2B illustrates a box plot of composite scores used to classify normal, LUNC or HCC patients. The figure represents data from the validation cohort with scores derived from the multiclass classifier.

FIGS. 2C-FIG. 2D show unsupervised hierarchical clustering of methylation markers differentially methylated between cancer (HCC and LUNC) and normal (FIG. 2C) and between HCC and LUNC (FIG. 2D). Each row represents an individual patient and each column represents a CpG marker.

FIG. 3A-FIG. 3C shows methylation profiling healthy control, high-risk patients and cancer patients. FIG. 3A shows that the methylation profiling differentiates HCC from high risk liver disease patients or normal controls. High risk liver diseases were defined as hepatitis, liver cirrhosis and fatty liver disease. FIG. 3B shows serum AFP differentiates HCC from high risk liver disease patients or normal controls. FIG. 3C shows methylation profiling differentiates LUNC from patients who smoke and normal controls.

FIG. 4A-FIG. 4L show cfDNA methylation analysis could predict tumor burden, staging, and treatment response using a composite diagnosis score in LUNC and HCC patients. (FIGS. 4A, 4E) cfDNA methylation analysis composite score (cd-score) in patients with and without detectable tumor burden; (FIGS. 4B, 4F) cd-score of patients with stage I/II and stage III/IV disease; (FIGS. 4C, 4G) cd-score in patients before surgery, after surgery, and with recurrence; (FIGS. 4D, 4H) cd-score in patients before treatment, with treatment response, and with worsening progression; (FIG. 4I) The ROC curve and the AUC of cd-score and AFP for HCC diagnosis in the entire HCC cohort; (FIG. 4J) AFP in patients with stage I/II and stage III/IV HCC; (FIG. 4K) AFP in HCC patients before surgery, after surgery, and with recurrence; (FIG. 4L) AFP in HCC patients before treatment, with treatment response, and with worsening progression. Recurrence was defined as tumor initially disappeared after treatment/surgery but recurred after a defined period. Progression was defined as worsening disease despite treatment/surgery.

FIG. 5A-FIG. 5F show prognostic prediction in HCC and LUNC survival based on cfDNA methylation profiling. (FIG. 5A) Overall survival curves of LUNC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation cohort (FIG. 5B) Overall survival curves of HCC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation dataset. (FIG. 5C) Survival curves of LUNC patients with stage I/II and stage III/IV in the validation cohort. (FIG. 5D) Survival curves of patients with stage I/II and stage III/IV HCC in the validation cohort. (FIGS. 5E, 5F) The ROC for 12 months survival predicted by cp-score, stage, and cp-score combined with stage of LUNC (FIG. 5E) and HCC (FIG. 5F) in the validation cohort.

FIG. 6A shows unsupervised hierarchical clustering of top 1000 methylation markers differentially methylated in DNA in HCC and LUNC tissues versus normal blood.

FIG. 6B shows unsupervised hierarchical clustering of the top 1000 methylation markers differentially methylated between HCC and LUNC tissue DNA. Each column represents an individual patient and each row represents a CpG marker.

FIG. 7A-FIG. 7B show an illustrative region encompassing two Blocks of Correlated Methylation (BCM) in cfDNA samples of from LUNC and HCC patients and normal controls. FIG. 7A shows the genomic neighborhood of the BCM displayed within UCSC genome browser (genome.ucsc.edu. Pearson correlation track showed correlation data by summing r values for a marker within a BCM. Cg marker names below the Pearson correlation graph (cg14999168, cg14088196, cg25574765) were methylation markers from TCGA. Gene name and common SNPs were also listed. FIG. 7B shows a not-to-scale representation of a set of analyzed cg markers belonging to two BCMs in this region. Boundaries between blocks are indicated by a black rectangle, whereas red squares indicate correlated methylation (r>0.5) between two nearby markers. Correlation between any two markers is represented by a square at the intersection of (virtual) perpendicular Lines originating from these two markers. White color indicates no significant correlation, 10 newly identified methylation markers in the left MCB anchored by marker cg14999168 or 11 newly identified methylation markers in the right MCB anchored by cg14088196/cg25574765 were highly consistent and correlated among HCC ctDNA, normal cfDNA, and HCC tissue DNA. Using markers within the same MCB can significantly enhanced allele calling accuracy. Vertical lines at the bottom of panel b were genomic coordinates of boundaries of two MCBs.

FIG. 8A shows unsupervised hierarchical clustering of 100 methylation markers selected for use in the diagnostic prediction model (cd-score) in the validation cohort.

FIG. 8B shows global view of supervised hierarchical clustering of all MCBs in the entire cfDNA dataset.

FIG. 9 shows box plots showing the behavior of 100 MCBs in the validation cohorts. Top plot: MCB mean values and deviations in the healthy patients vs patients with HCC or LUNC; bottom plot: MCB mean values and deviations in patients with LUNC vs HCC.

FIG. 10A-FIG. 10B show methylation values correlated with treatment outcomes in HCC and LUNC patients with serial plasma samples. FIG. 10A shows summary graphs of change in methylation value comparing patients after surgery, with clinical response (Partial Remission (PR) or Stable Disease (SD), or with disease progression/recurrent (PD). FIG. 10B shows methylation value trends in individual patients after complete surgical resection, with treatment response, and with disease progression. Delta methylation rare denotes the methylation value difference before treatment and after treatment. PRE: pre-treatment; POST: after-treatment.

FIG. 11A shows dynamic monitoring of treatment outcomes using the total methylation copy numbers of an MCB in LUNC patients.

FIG. 11B shows dynamic monitoring of treatment outcomes with the methylation value of an MCB in LUNC patients. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy.

FIG. 12A shows dynamic monitoring of treatment outcomes using the total methylation copy numbers of an MCB and AFP in HCC patients.

FIG. 12B show's dynamic monitoring of treatment outcomes with the methylation rate of an MCB in HCC patients. Dates of treatments are indicated in the figure. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy, TACE, trans-catheter arterial chemoembolization.

FIG. 13 illustrates a workflow for building the diagnostic and prognostic models. Whole genome methylation data on HCC, LUNC and normal blood were used to identify candidate markers for probe design. Left panel; diagnostic marker-selection: LASSO analysis was applied to a training cohort of 444 HCC, 299 LUNC, and 1123 normal patients to identify a final selection of 77 markers. These 77 markers were applied to a validation cohort of 445 HCC, 300 LUNC, and 1124 normal patients. Right panel: prognostic marker selection: LASSO-Cox were applied to a training cohort of 433 HCC and 299 LUNC patients with survival data to identify a final selection of 20 markers. These 20 markers were applied to a validation cohort of 434 HCC and 300 LUNC with survival data.

FIG. 14A-FIG. 14D illustrates cfDNA methylation analysis for diagnosis of LUNC and HCC. (FIG. 14A) Receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of the diagnostic prediction model (cd-score) using cfDNA methylation analysis in the validation cohort. (FIG. 14B) Box plot of composite scores used to classify normal and cancer patients (left), and LUNC and HCC patients (right). (FIG. 14C-FIG. 14D) Unsupervised hierarchical clustering of methylation markers differentially methylated between cancer (HCC and LUNC) and normal (FIG. 14C) and between HCC and LUNC (FIG. 14D). Each row represents an individual patient and each column represents a MCB marker.

FIG. 15A-FIG. 15D illustrates methylation profiling in healthy control, high-risk patients and cancer patients. (FIG. 15A) methylation profiling differentiates HCC from high risk liver disease patients or normal controls. High risk liver diseases were defined as hepatitis, liver cirrhosis and fatty liver disease. (FIG. 15B) Serum AFP differentiates HCC from high risk liver disease patients or normal controls. (FIG. 15C) methylation profiling differentiates LUNC from patients who smoke and normal controls. (FIG. 15D) Serum CEA differentiates LUNC from high risk (smoking) patients.

FIG. 16A-FIG. 16R illustrates cfDNA methylation analysis could predict tumor burden, staging, and treatment response using a composite diagnosis score in LUNC and HCC patients, cd-score m patients with and without detectable tumor burden in LUNC (FIG. 16A) when compared to CEA (FIG. 16I) and HCC (FIG. 16E) when compared to AFP (FIG. 16M); cd-score of patients with stage I/II and stage III/IV disease in LUNC (FIG. 16B) when compared to CEA (FIG. 16I) and HCC (FIG. 16F) patients when compared to AFP (FIG. 16N); cd-score in patients before intervention, after surgery, and with recurrence in LUNC (FIG. 16D) when compared to CEA (FIG. 16K) and HCC (FIG. 16G) when compared to AFP (FIG. 16D); cd-score in patients before intervention, with treatment response, and with worsening progression in LUNC (FIG. 16D) when compared to CEA (FIG. 16L) and HCC (FIG. 16H) when compared to AFP (FIG. 16P); (FIG. 16Q) The ROC curve and the AUC of cd-score and AFP for LUNC diagnosis in the entire LUNC cohort. (FIG. 16R) The ROC curve and the AUC of cd-score and AFP for HCC diagnosis in the entire HCC cohort.

FIG. 17A-FIG. 17F illustrates prognostic prediction in HCC and LUNC survival based on cfDNA methylation profiling Overall survival curves of HCC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation cohort (FIG. 17A). Overall survival curves of LUNC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation dataset (FIG. 17B). Survival curves of HCC patients with stage I/II and stage III/IV in the validation cohort (FIG. 17C). Survival curves of patients with stage I/II and stage III/IV LUNC in the validation cohort (FIG. 17D). FIG. 17E and FIG. 17F illustrate the ROC for 12 months survival predicted by cp-score, CEA, AFP, stage, and cp-score combined with stage of HCC (FIG. 17E) and LUNC (FIG. 17F) in the validation cohort.

FIG. 18A-FIG. 18B illustrates early detection of LUNC using a cfDNA methylation panel. 208 smoker patients were enrolled with lung nodules between 10 mm and 30 mm in size in a prospective trial and measured a cfDNA LUNC methylation panel Patients were divided into a training and a testing cohort (FIG. 18A); receiver operating characteristic t ROC) curves and the associated Area Under Curves (AUCs) of the prediction of Stage I LUNC versus benign lung nodules in the validation cohort with 91.4% accuracy (FIG. 18B); table showing prediction results between Stage I LUNC versus benign lung nodules showing high sensitivity and specificity in the validation cohort (FIG. 18C).

FIG. 19A-FIG. 19D illustrates methylation markers can differentiate between HCC and liver cirrhosis and Detect progression from liver cirrhosis to HCC. A prediction model was built using 217 HCC and 241 cirrhosis patients and divided patients into a training and a testing cohort (FIG. 19A); Receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of tltc prediction of Stage I HCC versus liver cirrhosis in the validation cohort with 89.9% accuracy (FIG. 19B); table showing prediction results between Stage I HCC and liver cirrhosis in a validation cohort (FIG. 19C); table showing prediction results on progression from liver cirrhosis to stage 1HCC with high sensitivity (89.5%) and specificity (98%) (FIG. 19D).

FIG. 20A illustrates unsupervised hierarchical clustering of top 1000 methylation markers differentially methylated in DNA in HCC and LUNC primary tissues versus normal blood.

FIG. 20B shows unsupervised hierarchical clustering of the top 1000 methylation markers differentially methylated between HCC and LUNC tissue DNA. Each column represents an individual patient and each row represents a CpG marker.

FIG. 20C illustrates a global view of supervised hierarchical clustering of all 888 MCBs in the entire cfDNA dataset.

FIG. 21 illustrates Boxplots showing the features of MCBs in cohorts. Top plot: Mean values and deviations of Lasso MCBs in each one versus rest comparison.

FIG. 22 illustrates methylation values correlated with treatment outcomes in HCC and LUNC patients with serial plasma samples. Summary graphs of change in methylation value comparing patients after surgery, with clinical response (Partial Remission (PR) or Stable Disease (SD), or with disease progression/recurrent (PD).

FIG. 23A shows dynamic monitoring of treatment outcomes using the total methylation copy numbers of an MCB in LUNC patients.

FIG. 23B shows dynamic monitoring of treatment outcomes with the methylation value of an MCB in LUNC patients. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy.

FIG. 24 illustrates dynamic monitoring of treatment outcomes using the total methylation copy numbers of an MCB and CEA in HCC patients.

FIG. 25 shows dynamic monitoring of treatment outcomes with the methylation rate of an MCB in HCC patients. Dates of treatments are indicated in the figure. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy, TACE, trans-catheter arterial chemoembolization.

FIG. 26 illustrates an exemplary workflow chart of data generation and analysis described herein.

FIG. 27A-FIG. 27H illustrates cfDNA methylation analysis for diagnosis of HCC. FIG. 27A shows heatmap of methylation of 28 pairs of matched HCC tumor DNA and plasma cfDNA, with a mean methylation value threshold of 0.1 as a cutoff. FIG. 27B shows the methylation values and standard deviations of ten diagnostic markers in normal blood, HCC tumor DNA, and HCC patient cfDNA. FIG. 27C shows the confusion table of binary results of the diagnostic prediction model in the training dataset and FIG. 27D shows the confusion table of binary results of the diagnostic prediction model in the validation dataset. FIG. 27E and FIG. 27F show ROC of the diagnostic prediction model with methylation markers in the training (FIG. 27E) and validation datasets (FIG. 27F). FIG. 27G and FIG. 27H show unsupervised hierarchical clustering of ten methylation markers selected for use in the diagnostic prediction model in the training (FIG. 27G) and validation datasets (FIG. 27H).

FIG. 28A-FIG. 28K shows cfDNA methylation analysis and tumor burden, treatment response, and staging. FIG. 28A and FIG. 28B shows the combined diagnosis score (cd-score) (FIG. 28A) and AFP (FIG. 28B) in healthy controls, individuals with liver diseases (HBV/HCV infection, cirrhosis, and fatty liver) and HCC patients. FIG. 28C shows the cd-score in normal controls and HCC patients with and without detectable tumor burden. FIG. 28D shows the cd-score in normal controls. HCC patients before treatment, with treatment response, and with progression, FIG. 28E shows the cd-score in normal controls and HCC patients before surgery, after surgery, and with recurrence. FIG. 28F shows the cd-score in normal controls and HCC patients from stage I-IV. FIG. 28G illustrates the ROC of cd-score and AFP for HCC diagnosis in whole HCC cohort. FIG. 28H and FIG. 28I show the cd-score (FIG. 28H) and AFP (FIG. 28I) in HCC patients with initial diagnosis (before surgery or other treatment), with treatment response, with progression, and with recurrence. FIG. 28) and FIG. 28K show the cd-score (FIG. 28J) and AFP (FIG. 28K) in HCC patients from stages I-IV.

FIG. 29A-FIG. 29G show the cfDNA methylation analysis for prognostic prediction HCC survival. FIG. 29A and FIG. 29B show the overall survival curves of HCC patients with low or high risk of death at 6 months, according to the combined prognosis score (cp-score) in the training (FIG. 29A) and validation datasets (FIG. 29B). FIG. 29C and FIG. 29D show the survival curves of HCC patients with stage I/II and stage III/IV in the training (FIG. 29C) and validation datasets (FIG. 29D). FIG. 29E and FIG. 29F show the ROC for the cp-score, stage, and cp-score combined with stage in the training (FIG. 29E) and validation datasets (FIG. 29F). FIG. 29G shows the survival curves of HCC patients with combinations of cp-score risk and stage in the whole HCC cohort.

FIG. 30 illustrates an unsupervised hierarchical clustering of top 1000 methylation markers differentially methylated between HCC tumor DNA and normal blood. Each column represents an individual patient and each row represents a CpG marker.

FIG. 31A-FIG. 31B illustrate an exemplary region encompassing two Blocks of Correlated Methylation (BCM) in cfDNA samples of from HCC and normal controls. FIG. 31A shows a genomic neighborhood of the BCM displayed within UCSC genome browser (Pearson correlation track showed correlation data by summing r values for a marker within a BCM. Cg marker names below the Pearson correlation graph (cg14999168, cg14088196, cg25574765) were methylation markers from TCGA. Gene name and common SNPs were also listed. FIG. 31B shows a not-to-scale representation of a set of analyzed cg markers belonging to two BCMs in this region. Boundaries between blocks are indicated by a black rectangle, whereas red squares indicate correlated methylation (r>0.5) between two nearby markers. Correlation between any two markers is represented by a square at the intersection of (virtual) perpendicular lines originating from these two markers. White color indicates no significant correlation. 10 newly identified methylation markers in the left MCB anchored by marker cg14999168 or 11 newly identified methylation markers in the right MCB anchored by cg14088196/cg 25574765 were highly consistent and correlated among HCC ctDNA, normal cfDNA, and HCC tissue DNA. Using markers within the same MCB can significantly enhanced allele calling accuracy. Vertical lines at the bottom of panel b were genomic coordinates of boundaries of two MCBs.

FIG. 32A-FIG. 32B show methylation values correlated with treatment outcomes in HCC patients with serial plasma samples. FIG. 32A shows a change in cd-score comparing patients after surgery, with clinical response, and with disease progression (*** p<0.001). FIG. 32B shows cd-score trends in individual patients after complete surgical resection with treatment response, and with disease progression. PRE: pre-treatment; POST: after-treatment.

FIG. 33 illustrates a dynamic monitoring of treatment outcomes in individual patients with cd-score and AFP. Dates of treatments are indicated by vertical blue arrows. PD, progressive disease; PR partial response; SD, stable disease; TACE, trans-catheter arterial chemoembolization.

FIG. 34A-FIG. 34B illustrate cfDNA methylation analysis of HCC diagnosis. FIG. 34A shows heat map of methylation of 28 pairs of matched HCC tumor DNA and plasma cfDNA, with a mean methylation value threshold of 0.1 as a cutoff. FIG. 34B shows the methylation values and standard deviations often diagnostic markers in normal plasma. HCC tumor DNA, and HCC patient cfDNA.

FIG. 35 illustrates hierarchical clustering of 10 diagnostic markers in TCGA HCC tissue DNA, Chinese HCC tissue DNA, HCC ctDNA and normal plasma cfDNA. Each column represents an individual patient and each row represents a CpG marker. Clustering within groups shows that beta values follow a gradient between primary HCC tissue and normal cfDNA, with tumor cfDNA showing intermediate beta values. cfDNA of samples with higher stage tumors have beta values approaching primary HCC tissue samples.

FIG. 36 shows dynamic monitoring of treatment outcomes in individual patients with methylation composite values and AFP. Dates of treatments are indicated by vertical blue arrows. PD, progressive disease: PR partial response; SD, stable disease; TACE, trans-catheter arterial chemoembolization.

FIG. 37A-FIG. 37B show TNM stage for prognostic prediction HCC survival. Survival curves of HCC patients with stage I/II and stage III/IV in the training (FIG. 37A) and validation datasets (FIG. 37B).

FIG. 38A-FIG. 38C show mixing experiment to measure cfDNA fractions from tumor tissue genomic DNA (gDNA). FIG. 38A shows digital PCR (ddPCR) results. Upper panel, each blue dot represents a single PCR reaction and one methylated DNA molecule. Lower panel, each green dot represents a single PCR reaction and one unmethylated DNA molecule. A01 to A08 denoted a mixing experiment (A01, 100% normal cfDNA, 0% HCC gDNA; A02, 90% normal cfDNA, 10% HCC gDNA; A04, 70% normal cfDNA, 30% HCC gDNA; A07, 40% normal cfDNA, 60% HCC gDNA; A08, 0% normal cfDNA, 100% HCC gDNA. See h and c for a summary of the results. FIG. 38B shows mixing experiment results with different fraction of normal cfDNA and HCC (gDNA). FIG. 38C shows illustration of final methylation values of HCC cfDNA as a product of mixing normal cfDNA and pure HCC gDNA (ca_gDNA), Each vertical line represents a mixing experiment (from left to right, A01, A02, A04, A07, A08). Green dots and bars corresponding to the left Y axis (Ch1) represent mean values and standard error (S.E.) of unmethylated values; Blue dots and bars corresponding to the right Y axis (Ch2) represent mean values and S.E. of methylated values. The X axis denotes varying concentrations of normal cfDNA and HCC gDNA (see FIG. 38A for details).

FIG. 39A-FIG. 39C show measurements of cfDNA concentrations in normal and HCC plasma samples. FIG. 39A shows 11±4.7 ng cfDNA per 1 mL of normal plasma and 22±7.7 ng cfDNA per 1 mL of HCC plasma. The data were shown as mean±standard deviation (X±SD). P<0.001 (student t-test). FIG. 39B illustrates a plot demonstrating a linear relationship between amount of DNA and total copy numbers by digital droplet PCR (methylated plus unmethylated) (P<0.001). FIG. 39C shows comparison of cfDNA yields by different commercial extraction kits. Elitehealth and Qiagen kits gave higher and comparable yields (No significant difference in yield between Elitehealth and Qiagen, P>0.05 there was significant difference in yield between. EliteHealth versus Thermo Fisher, P<0.05, or between Qiagen versus Thermo Fisher, P<0.05).

DETAILED DESCRIPTION OF THE DISCLOSURE

Cancer is characterized by an abnormal growth of a cell caused by one or more mutations or modifications of a gene leading to dysregulated balance of cell proliferation and cell death. DNA methylation silences expression of tumor suppression genes, and presents itself as one of the first neoplastic changes. Methylation patterns found in neoplastic tissue and plasma demonstrate homogeneity, and in some instances are utilized as a sensitive diagnostic marker. For example, cMethDNA assay has been shown in one study to be about 91% sensitive and about 96% specific when used to diagnose metastatic breast cancer. In another study, circulating tumor DNA (ctDNA) was about 87.2% sensitive and about 99.2% specific when it was used to identify KRAS gene mutation in a large cohort of patients with metastatic colon cancer (Bettegowda et al., Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci. Transl. Med, 6(224):ra24, 2014). The same study further demonstrated that ctDNA is detectable in >75% of patients with advanced pancreatic, ovation, colorectal, bladder, gastroesophageal, breast, melanoma, hepatocellular, and head and neck cancers (Bettegowda et al).

Additional studies have demonstrated that CpG methylation pattern correlates with neoplastic progression. For example, in one study of breast cancer methylation patterns, P16 hypermethylation has been found to correlate with early stage breast cancer, while TIMP3 promoter hypermethylation has been correlated with late stage breast cancer. In addition, BMP6, CST6 and TIMP3 promoter hypermethylation have been shown to associate with metastasis into lymph nodes in breast cancer.

In some embodiments, DNA methylation profiling provides higher clinical sensitivity and dynamic range compared to somatic mutation analysis for cancer detection. In other instances, altered DNA methylation signature has been shown to correlate with the prognosis of treatment response for certain cancers. For example, one study illustrated that in a group of patients with advanced rectal cancer, ten differentially methylated regions were used to predict patients' prognosis. Likewise, RASSFIA DNA methylation measurement in serum was used to predict a poor outcome it patients undergoing adjuvant therapy in breast cancer patients in a different study. In addition, SRBC gene hypermethylation was associated with poor outcome in patients with colorectal cancer treated with oxidiplatin in a different study. Another study has demonstrated that ESR1 gene methylation correlate with clinical response in breast cancer patients receiving tamoxifen. Additionally. ARHI gene promoter hypermethylation was shown to be a predictor of long-term survival in breast cancer patients not treated with tamoxifen.

In some embodiments, disclosed herein are methods and kits of diagnosing lung cancer and hepatocellular carcinoma (HCC) based on DNA methylation profiling. In some instances, provided herein are methods and kits of distinguishing between lung cancer and HCC based on the DNA methylation profiling. In other instances, provided herein are methods and kits of identifying a subject has having lung cancer or HCC based on the DNA methylation profiling. In additional instances, provided herein are methods and kits of determining the prognosis of a subject having lung cancer or HCC and determining the progression of lung cancer or HCC in a subject based on the DNA methylation profilings.

Methods of Use Methods of Diagnosis of a Subject

Disclosed herein, in certain embodiments, are methods of diagnosing hepatocellular carcinoma (HCC) and selecting subjects suspected of having liver cancer for treatment, in some instances, the methods comprise utilizing one or more biomarkers described herein. In some instances, a biomarker comprises a cytosine methylation site. In some instances, cytosine methylation comprises 5-methylcytosine (5-mCyt) and 5-hydroxymethylcytosine. In some cases, a cytosine methylation site occurs in a CpG dinucleotide motif. In other cases, a cytosine methylation site occurs in a CHG or CHH motif, in which H is adenine, cytosine or thymine. In some instances, one or more CpG dinucleotide motif or CpG sire forms a CpG island, a short DNA sequence rich in CpG dinucleotide, in some instances, CpG islands are typically, but not always, between about 0.2 to about 1 kb in length. In some instances, a biomarker comprises u CpG island.

In some embodiments, disclosed herein is a method of selecting a subject suspected of having hepatocellular carcinoma (HCC) or lung cancer for treatment, comprising (a) contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of n gene selected from Tables 2, 6, 7, 9, or 10 to generate an amplified product, wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified product to generate a methylation profile of the gene; (c) applying the methylation profile to a model relating methylation profiles of genes from tin; gene panel to the presence to HCC or lung cancer; (d) evaluating an output from the model to determine whether the subject has HCC or lung cancer; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC or lung cancer.

In some embodiments, one or more genes in a gene panel described herein further comprises a methylation correlated block (MCB) In some cases, a MCB comprises about 2 to about 30, about 2 to about 25, about 2 to about 20, about 2 to about 15, about 2 to about 10, about 2 to about 8, or about 2 to about 5 genes. In some instances, a MCB comprises about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, or more genes.

In some instances, MCB comprises one or more genes selected from Tables 2, 3, 6, 7, 9, or 10. In some instances, MCB comprises one or more genes selected from Table 2. In some instances, MCB comprises one or more genes selected from Table 3. In some instances, MCB comprises one or more genes selected from T able 6. In some instances, MCB comprises one or more genes selected from Table 7. In some instances, MCB comprises, one or more genes selected from Table 9. In some instances, MCB comprises one or more genes selected from Table 10.

In some instances, MCB comprises one or more genes selected from SOCS2, EPSTI1, TIA1, Chromosome 4, Chromosome 6, ZNF323, FOXP4, GRHL2, NPBWR1, Chromosome 2, AAK1, SIM1, C10orf46, C17orf101, DEPDC5, ZNF323, GABRA2, PLAC8, and ADRA2B. In some instances, MCB comprises one or more genes selected from SOCS2, EPSTI1, TIA1, Chromosome 4, Chromosome 6, ZNF323, FOXP4, and GRHL2. In some instances, MCB comprises one or more genes selected from NPBWR1, Chromosome 2, AAK1, SIM1, C10orf46, C17orf101, DEPDC5, ZNF323, GABRA2, PLAC8, and ADRA2B. In some instances, MCB comprises SOCS2. In some instances, MCB comprises EPSTI1. In some instances, MCB comprises TIA1. In some instances, MCB comprises Chromosome 4. In some instances, MCB comprises Chromosome 6. In some instances, MCB comprises ZNF323. In some instances, MCB comprises FOXP4. In some instances, MCB comprises NPBWR1. In some instances, MCB comprises GRHL2. In some instances, MCB comprises Chromosome 2. In some instances, MCB comprises AAK1. In some instances, MCB comprises SIM1. In some instances, MCB comprises C10orf46. In some instances, MCB comprises C17orf101. In some instances, MCB comprises DEPDC5. In some instances, MCB comprises ZNF323. In some instances, MCB comprises GABRA2. In some instances, MCB comprises PLAC8. In some instances, MCB comprises ADRA2B.

In some instances, the method further comprises contacting the treated DNA with at least an additional probe that hybridizes under high stringency conditions to a target sequence of an additional gene selected from the gene panel to generated an additional amplified product, and analyze the additional amplified product to generate a methylation profile of the additional gene, thereby determining the presence of HCC or king cancer in the subject.

In some cases, the biological sample is treated with a deaminating agent to generated the treated DNA.

In some cases, the model comprises methylation profiles of genes from the gene panel generated from an HCC positive sample or a lung cancer positive sample. In some instances, the HCC positive sample comprises cells from a metastatic, HCC, In some cases, the lung cancer positive sample comprises cells from metastatic lung cancer.

In some cases, the model further comprises methylation profiles of genes from the gene panel generated from a normal sample.

In some cases, the model is developed based on the methylation profiles of biomarkers from Tables 3, 6 or 7.

In some cases, the model is developed based on the methylation profiles of biomarkers from Tables 9 or 10.

In some cases, the model is developed using an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

In some cases, the method further comprises distinguishing between HCC and lung, cancer.

Hepatocellular carcinoma (HCC or malignant hepatoma), in some instances, is the most common type of liver cancer. In some cases, HCC is the result of a viral hepatitis infection (e.g., by either Hepatitis B or C), metabolic toxins (e.g., alcohol or aflatoxin), non-alcoholic fatty liver disease (NASH), hemochromatosis, or alpha 1-antitrypsin deficiency.

In some embodiments, HCC is further classified by staging. In some instances, the staging takes into account tumor size, tumor number, presence of vascular invasion and extrahepatic spread, liver function (levels of serum bilirubin and albumin, presence of ascites and portal hypertension) and health of the patient. In some cases, the staging is the Barcelona Clinical Liver Cancer (BCLC) staging classification. In some cases, the staging of HCC comprises Stage I, Stage II, Stage III and Stage IV.

In some instances, one or inure markers described herein is utilized to diagnose a subject as having HCC. In some cases, HCC, is a metastasized HCC. In some instances, one or more markers described herein is utilized to distinguish between HCC and lung cancer in a subject.

In some embodiments, a lung cancer is any type of lung cancer. In some instances, a lung cancer comprises non-small cell lung cancer (NSCLC), small cell king cancer (SCLC), bronchial carcinoids or mesothelioma. In some instances, non-small cell lung cancer comprises adenocarcinoma, squamous cell carcinoma, large cell carcinoma or large cell neuroendocrine tumors. In some cases, the lung cancer is a metastasized lung cancer. In other cases, the lung cancer is a relapsed or refractory lung cancer.

In some embodiments, a probe comprises a DNA probe, RNA probe, or a combination thereof. In some instances, a probe comprises natural nucleic acid molecules and non-natural nucleic acid molecules. In some cases, a probe comprises a labeled probe, such as for example, fluorescently labeled probe or radioactively labeled probe. In some instances, a probe correlates to a CpG site. In some instances, a probe is utilized in a next generation sequencing reaction to generate a CpG methylation data. In further instances, a probe is used in a solution-based next generation sequencing reaction to generate a CpG methylation data. In some cases, a probe comprises a molecular beacon probe, a TaqMan probe, locked nucleic acid probe, a padlock probe, or Scorpion probe. In some cases, a probe comprises a padlock probe.

In some instances, the treatment comprises transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy.

In some instances, the treatment comprises a chemotherapeutic agent or an agent for a targeted therapy. In some cases, the chemotherapeutic agent comprises cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof. In some cases, the agent for the targeted therapy comprises axitinib, bevacizumab, cetuximab, erlotinib, ramucirumab, regorafenib, sorafenib, sunitinib, a thymidine kinase (TK) inhibitor, or a combination thereof.

In some embodiments, the treatment comprises surgery. In some cases, surgery comprises curative resection. In other cases, surgery comprises liver transplantation.

In some embodiments, the biological sample comprises a blood sample.

In some cases, the biological sample comprises a tissue biopsy sample.

In some cases, the biological sample comprises circulating tumor cells.

In some instances, the subject is a human.

HCC Gene Panel

In some embodiments, disclosed herein is a method of selecting a subject method of selecting a subject suspected of having hepatocellular carcinoma (HCC) for treatment, comprising: (a) contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of gene selected from a gene panel consisting of bone morphogenetic protein receptor type 1A (BMPR1A), Pleckstrin Homology and SEC7 domain containing protein 1 (PSD), Rho GTPase activating protein 25 (ARHGAP25), Kruppel like factor 3 (KLF3), placenta specific 8 (PLAC8), ataxin 1 (ATXN1), Chromosome 6:170, Chromosome 6:3, ATPase family AAA domain-containing protein 2 (ATAD2), and Chromosome 8:20 to generate an amplified product, wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified product to generate a methylation profile of the gene; (c) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC; (d) evaluating an output from the model to determine whether the subject has HCC; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC.

In some embodiments, also disclosed herein is a method of selecting a subject suspected of having hepatocellular carcinoma (HCC) for treatment, comprising: (a) contacting treated DNA with a plurality of probes to generate amplified products, wherein each probe hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20, and each probe is different, and wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified products to generate a methylation profile of the genes from the gene panel; (c) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC; (d) evaluating an output from the model to determine whether the subject has HCC; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC.

In additional embodiments, disclosed herein is a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In additional embodiments, disclosed herein is a method of defecting the methylation status of one or more genes of a gene panel in a subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; and (b) detecting the methylation status in a gene selected from the gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20 by contacting the treated DNA with a probe dun hybridizes under high stringency conditions to a target sequence of the gene to generate an amplified product; and analyzing the amplified product to determine the methylation status of the gene.

In some embodiments, one or more genes in the gene panel further comprises a methylation correlated block (MCB). In some cases, a MCB comprises about 2 to about 30, about 2 to about 25, about 2 to about 20, about 2 to about 15, about 2 to about 10, about 2 to about 8, or about 2 to about 5 genes. In some instances, a MCB comprises about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, or more genes.

In some instances, MCB comprises one or more genes selected from BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, Chromosome 8:20, SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B. In some instances, a MCB comprises one or more genes selected front BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20. In some instances, a MCB comprises one or more genes selected from SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B. In some instances, a MCB comprises BMPR1A. In some instances, a MCB comprises PSD. In some instances, a MCB comprises KLF3. In some instances, a MCB comprises PLAC8. In some instances, a MCB comprises ATXN1. In some instances, a MCB comprises Chromosome 6:170. In some instances, a MCB comprises Chromosome 6:3. In some instances, a MCB comprises ATAD2. In some instances, a MCB comprises Chromosome 8:20. In some instances, a MCB comprises SH3PXD2A. In some instances, a MCB comprises C11orf9. In some instances, a MCB comprises PPFIA1. In some instances, a MCB comprises Chromosome 17:78. In some instances, a MCB comprises ARHGAP25. In some instances, a MCB comprises SERPINB5. In some instances, a MCB comprises NOTCH3. In some instances, a MCB comprises GRHL2. In some instances, a MCB comprises TMEM8B.

In some instances, the method further comprises contacting the treated DNA with at least an additional probe that hybridizes under high stringency conditions to a target sequence of an additional gene selected from the gene panel to generated an additional amplified product, and analyze the additional amplified product to generate a methylation profile of the additional gene, thereby determining the presence of HCC in the subject.

In some cases, the biological sample is treated with a deaminating agent to generated the treated DNA.

In some cases, the model comprises methylation profiles of genes from the gene panel generated from an HCC positive sample. In some instances, the HCC positive sample comprises cells from a metastatic HCC.

In some cases, the model further comprises methylation profiles of genes from the gene panel generated from a normal sample.

In some cases, the model Is developed baser on the methylation profiles of biomarkers from Table 15 or Table 16.

In some cases, the model is developed using an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

Hepatocellular carcinoma (HCC or malignant hepatoma), in some instances, is the most common type of liver cancer. In some cases, HCC is the result of a viral hepatitis infection (e.g., by either Hepatitis B or C), metabolic toxins (e.g., alcohol or aflatoxin), non-alcoholic fatty liver disease (NASH), hemochromatosis, or alpha 1-antitrypsin deficiency.

In some embodiments, HCC is further classified by staging. In some instances, the staging takes into account tumor size, tumor number, presence of vascular invasion and extrahepatic spread, liver function (levels of serum bilirubin and albumin, presence of ascites and portal hypertension) and health of the patient. In some cases, the staging is the Barcelona Clinical Liver Cancer (BCLC) staging classification. In some cases, the staging of HCC comprises Stage I, Stage II, Stage III and Stage IV.

In some embodiments, one or more biomarkers described herein distinguishes HCC from another type of liver cancer. In some cases, another type of liver cancer comprises cirrhosis of the liver or hepatic steatosis (or fatty liver disease).

In some embodiments, a probe comprises a DNA probe, RNA probe, or a combination thereof. In some instances, a probe comprises natural nucleic acid molecules and non-natural nucleic acid molecules. In some cases, a probe comprises a labeled probe, such as for example, fluorescently labeled probe or radioactively labeled probe. In some instances, a probe correlates to a CpG site. In some instances, a probe is utilized in a next generation sequencing, reaction to generate a CpG methylation data. In further instances, a probe is used in a solution-based next generation sequencing reaction to generate a CpG methylation data. In some cases, a probe comprises a molecular beacon probe, a TaqMan probe, locked nucleic acid probe, a padlock probe, or Scorpion probe. In some cases, a probe comprises a padlock probe. In some cases, the probe is a padlock probe.

In some instances, the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ NOs: 1-18. In some instances, the probe comprises about 90%, 94%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to a probe selected from SEQ NOs: 1-18. In some instances, the probe comprises about 90% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises abort 91% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 92% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 93% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 94% sequence identity to a probe selected from SEQ NOs: 1-18, in some instances, the probe comprises about 95% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 96% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 97% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 98% sequence identity to a probe selected from SEQ ID NOs: 1-18. In some instances, the probe comprises about 99% sequence identity to a probe selected from SEQ ID NOs: 1-18.

In some instances, the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 90% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 91% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 92% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 93% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 94% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 95% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 96% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 97% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 98% sequence identity to a probe selected from SEQ ID NOs: 1-10. In some instances, the probe comprises about 99% sequence identity to a probe selected from SEQ ID NOs: 1-10.

In some instances, the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 90% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 91% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 92% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 93% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 94% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 95% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 96% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 97% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 98% sequence identity to a probe selected from SEQ ID NOs: 11-18. In some instances, the probe comprises about 99% sequence Identity to a probe selected from SEQ ID NOs: 11-18.

In some instances, the treatment comprises transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy.

In some instances, the treatment comprises a chemotherapeutic agent or an agent for a targeted therapy. In some cases, the chemotherapeutic agent comprises cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof. In some cases, the agent for the targeted therapy comprises axitinib, bevacizumab, cetuximab, erlotinib, ramucirumib, regorafenib, sorafenib, sunitinib, a thymidine kinase (TK) inhibitor, or a combination thereof.

In some embodiments, the treatment comprises surgery. In some cases, surgery comprises curative resection. In other cases, surgery comprises liver transplantation.

In some embodiments, the biological sample comprises a blood sample.

In some cases, the biological sample comprises a tissue biopsy sample.

In some cases, the biological sample comprises circulating tumor cells.

In some instances, the subject is a human.

Determining the Prognosis of a Subject or Monitoring the Progression of HCC or Lung Cancer in a Subject

In some embodiments, disclosed herein include a method of determining the prognosis of a subject having HCC or lung cancer or monitoring the progression of HCC or lung cancer in a subject. In some embodiments, disclosed herein is a method of determining the prognosis of a subject having hepatocellular carcinoma (BCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from suppressor of cytokine signaling 2 (SOCS2), epithelial stromal interaction protein 1 (EPSTI1), cytotoxic granule associated RNA binding protein (TIA1), Chromosome 4, chromosome 6, zinc finger and SCAN domain containing 31 (ZNF323), forkhead box P4 (FOXP4), and grainyhead like transcription factor 2 (GRHL2) from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject, has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In some embodiments, disclosed herein is a method of determining the prognosis of a subject having lung cancer or monitoring the progression of lung cancer in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from neuropeptides B/W receptor 1 (NPBWR1), Chromosome 2, AP2 associated kinase 1 (AAK1), single-minded family BHLH transcription factor 1 (SIM1), C10orf46, C17orf101, DEP domain containing 5 (DEPDC5), zinc finger and SCAN domain containing 31 (ZNF323), gamma-aminobutyric acid type A receptor alpha2 subunit (GABRA2), placenta specific 8 (PLAC8), and adrenoceptor alpha 2B (ADRA2B) from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In additional embodiments, disclosed herein is a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a do aminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from SH3 and PX domains 2A (SH3PXD2A), C11orf9, PTPRF interacting protein alpha 1 (PPFIA1), Chromosome 17:78, Serpin family B member 5 (SERPINB5), neurogenic locus Notch homolog protein 3 (NOTCH3), grainyhead like transcription factor 2 (GRHL2), and transmembrane protein 8B (TMEM8B) from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

In some instances, a methylation score is utilized to determine the prognosis of a subject. In some instances, the methylation score is further refers to herein as a combined prognosis score (cp-score). In some instances, prognosis refers to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of liver cancer. The term “prediction” is used herein to refer to the likelihood that a subject will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a subject will survive, following chemotherapy for a certain period of time without cancer recurrence end/or following surgery (e.g., removal of the spleen). In some instances, a methylation score is utilized to determine the prognosis of a subject having liver cancer.

In some embodiments, a methylation score (or cp-score) correlates with a “good” prognosis. In some instances, a “good” prognosis refers to the likelihood that a subject will likely respond favorably to a drug or set of drugs, leading to a complete or partial remission of liver cancer or a decrease and/or a stop in the progression liver cancer. In some instances, a “good” prognosis refers to d survival of subject of from at least 1 month to at least 90 years. In some instances, a “good” prognosis refers to the survival of a subject in which the survival of the subject upon treatment is from at least 1 month to at least 90 years. In some instances, the survival of a subject further refers to an extended survival rate of a subject receiving a treatment course relative to a subject without receiving the same course of treatment. In some cases, a “good” prognosis refers to an extended survival time of a subject receiving a treatment course relative to a subject without receiving the sang course of treatment.

In some instances, a methylation score (or cp-score) correlates to a survival from at least 0.1 month to at least 90 years. In some instances, a methylation score of from about 1.5 to about 4 is indicative of a survival of at toast 2 months, 4 months, 6 months, 8 months, 10 months, 1 year, 1.5 years, 2 years, 3 years, 4 years, 5 years, 10 years, 15 years, 20 years, 30 years, 50 years, or more.

In some embodiments, a methylation score (or cp-score) correlates with a “poor” prognosis. In some instances, a “poor” prognosis refers to the likelihood that a subject will likely respond unfavorably to a drug or set of drugs, leading to a progression of leukemia (e.g., progression to metastatic leukemia) and/or to refractory of one or more therapeutic agents. In some instances, a “poor” prognosis refers to the likelihood that a subject will not respond to a drug or set of drugs, leading to a progression of leukemia. In some instances, a “poor” prognosis refers to the survival of a subject of from less than 5 years to less than 1 month. In some instances, a “poor” prognosis refers to the survival of a subject in which the survival of the subject upon treatment is from less than 5 years to less than 1 month. In some instances, a “poor” prognosis further refers to the likelihood that a subject will develop a refractory leukemia toward one or more drugs.

In some instances, a methylation score (or cp-score) correlates with a survival of from less than 5 years to less than 0.1 month. In some instances, a methylation score of less than 1.5 is indicative of a survival of less than 5 years, 4 years, 3 years, 2 years, 1.5 years, 1 year, 10 months, 8 months, 6 months, 4 months, or 2 months.

In some embodiments, the methylation score is calculated, e.g., based on model for a survival analysts. In some instances, a survival analysis is a statistic analysis for analyzing the expected duration of time until one or more events of interest happen. In some instances, survival analysis comprises Cox proportional hazards (pH) regression analysis, log-rank test or a product limit estimator. In some instances, the methylation score is calculated based on Cox proportional hazards (PH) regression analysis, log-rank test or product limit estimator. In some instances, the methylation score is calculated based on Cox proportional hazards (PH) regression analysis. In some embodiments, the methylation score is further calculated based on a log-rank test. In some instances, the log-rank test is a hypothesis test to compare the survival distribution of two samples (e.g., a training set and a validation sit) In some instances, the log-rank test is also referred to as a Mantel-Cox test of a time-stratified Cochran-Mantel-Haenszel test. In some instances, the methylation score is additionally calculated based on a product limit estimator, A product limit estimator (also known as Kaplan-Meier estimator) is a non parametric statistic used to estimate the survival function from lifetime data. In some embodiments, the methylation score is initially calculated based an Cox proportional hazards (PH) regression analysis and then reprocessed with a log-rank test.

Detection Methods

In some embodiments, a number of methods are utilized to measure, detect, determine, identify, and characterize the methylation status/level of a gene or a biomarker CpG island-containing region/fragment) in identifying a subject as having liver cancer, determining the liver cancer subtype, the prognosis of a subject having liver cancer, and the progression or regression of liver cancer in subject in the presence of a therapeutic agent.

In some instances, the methylation profile is generated from a biological sample isolated from an individual. In some embodiments, the biological sample is a biopsy. In some instances, the biological sample is a tissue sample. In some instances, the biological sample is a tissue biopsy sample. In some instances, the biological sample is a blood sample. In other instances, the biological sample is a cell-free biological sample. In other instances, the biological sample is a circulating tumor DNA sample. In one embodiment, the biological sample is a cell free biological sample containing circulating tumor DNA.

In some embodiments, a biomarker (or an epigenetic marker) is obtained from a liquid sample. In some embodiments, the liquid sample comprises blood and other liquid samples of biological origin (including, but not limited to, peripheral blood, sera, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, hone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, bronchioalveolar lavage fluid, semen, prostatic fluid, cowper's fluid or pre-ejaculatory fluid, female ejaculate, sweat, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, ascites, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions/flushing, synovial fluid, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates, blastocyl cavity fluid, of umbilical cord blood. In some embodiments, the biological fluid is blood, a blood derivative or a blood fraction, e.g., serum or plasma. In a specific embodiment, a sample comprises a blood sample. In another embodiment, a serum sample is used. In another embodiment, a sample comprises urine. In some embodiments, the liquid sample also encompasses a sample that has been manipulated in any way after their procurement, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washed, or enriched for certain cell populations.

In some embodiments, a biomarker (or an epigenetic marker) is obtained from a tissue sample. In some instances, a tissue, corresponds to any cells(s). Different types of tissue correspond to different types of cells (e.g., liver, lung, blood, connective tissue, and the like), but also healthy cells vs tumor cells or to tumor cells at various stages of neoplasia, or to displaced malignant tumor cells. In some embodiments, a tissue sample Maher encompasses a clinical sample, and also includes cells in culture, cell supernatants, organs, and the like. Samples also comprise fresh-frozen and/or formalin-fixed, paraffin-embedded tissue blocks, such as blocks prepared from clinical or pathological biopsies, prepared for pathological analysis or study by immunohistochemistry.

In some embodiments, a biomarker (or an epigenetic marker) is methylated or unmethylated in a normal sample (e.g., normal or control tissue without disease, or normal or control body fluid, stool, blood, serum, amniotic fluid), most importantly in healthy stool, blood, serum, amniotic fluid or other body fluid. In other embodiments, a biomarker (or an epigenetic marker) is hypomethylated or hypermethylated in a sample from a patient having or at risk of a disease (e.g., one or more indications described herein); for example, at a decreased or increased (respectively) methylation frequency of at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or about 100% in comparison to a normal sample. In one embodiment, a sample is also hypomethylated or hypermethylated in comparison to a previously obtained sample analysis of the same patient having or at risk of a disease (e.g., one or more indications described herein), particularly to compare progression of a disease.

In some embodiments, a methylome comprises a set of epigenetic markers or biomarkers, such as a biomarker described above. In some instances, a methylome that corresponds to the methylome of a tumor of an organism (e.g., a human) is classified as a tumor methylome. In some cases, a tumor methylome is determined using tumor tissue or cell-free (or protein-free) tumor DNA in a biological sample. Other examples of methylomes of interest include the methylomes of organs that contribute DNA into a bodily fluid (e.g. methylomes of tissue such as brain, breast, lung, the prostrate and the kidneys, plasma, etc.).

In some embodiments, a plasma methylome is the methylome determined from the plasma or serum of an animal (e.g., a human). In some instances, the plasma methylome is an example of a cell-free or protein-free methylome since plasma and serum include cell-free DNA. The plasma methylome is also an example of a mixed methylome since it is a mixture of tumor and other methylomes of interest. In some instances, the urine methylome is determined from, the urine sample of a subject. In some cases, a cellular methylome corresponds to the methylome determined from cells (e.g., blood cells) of the patient. The methylome of the blood cells is called the blood cell methylome (or blood methylome).

In some embodiments, DNA (e.g., genomic DNA such as extracted gnomic DNA or treated genomic DNA) is isolated by any means standard in the art, including the use of commercially available kits. Briefly, wherein the DNA of interest is encapsulated in by a cellular membrane the biological sample as disrupted and lysed by enzymatic, chemical or mechanical means. In some cases, the DNA solution is then cleared of proteins and other contaminants e.g. by digestion with proteinase K. The DNA is then recovered from the solution. In such cases, this is carried out by means of a variety of methods including salting out, organic extraction or binding of the DNA to a solid phase support. In some instances, the choice of method is affected by several factors including time, expense and required quantity of DNA.

Wherein the sample DNA is not enclosed in a membrane (e.g. circulating DNA from a cell free sample such as blood or urine) methods standard in the art for the isolation and/or purification of DNA are optionally employed (See, for example, Bettegowda et al. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies, Sci. Transl. Med, 6(224): ra24. 2014). Such methods include the use of a protein degenerating reagent e.g. chaotropic salt e.g. guanidine hydrochloride or urea; or a detergent e.g. sodium dodecyl sulphate (SDS), cyanogen bromide. Alternative methods include but are not limited to ethanol precipitation or propanol precipitation, vacuum concentration amongst others by means of a centrifuge. In some cases, the person skilled in the art also make use of devices such as filter devices e.g. ultrafiltration, silica surfaces or membranes, magnetic particles, polystyrol particles, polystyrol surfaces, positively charged surfaces, and positively charged membranes, charged membranes, charged surfaces, charged switch membranes, charged switched surfaces.

In some instances, once the nucleic acids have been extracted, methylation analysis k carried out by any means known in the art. A variety of methylation analysis procedures are known in the art and may be used to practice the methods disclosed herein. These assays allow for determination of the methylation state of one or a plurality of CpG sites within a tissue sample. In addition, these methods may be used for absolute or relative quantification of methylated nucleic acids. Such methylation assays involve, among other techniques, two major steps. The first step is a methylation specific reaction or separation, sorb as (i) bisulfite treatment, (ii) methylation specific binding, or (iii) methylation specific restriction enzymes. The second major step involves (i) amplification and detection, or ail direct detection, by a variety of methods such as (a) PCR (sequence-specific amplification) such as Taqman®, (b) DNA sequencing of untreated and bisulfite-treated DNA, (c) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (d) pyrosequencing, (e) single-molecule sequencing, (f) mass spectroscopy, or (g) Southern blot analysis.

Additionally, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA may be used. e.g., the method described by Sadri and Hornsby (1996, Nucl. Acids Res. 24:5058-5059), or COBRA (Combined Bisulfite Restriction Analysis) (Xiong and Laird, 1997, Nucleic Acids Res. 25:2532-2534). COBRA analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific gene loci in small amounts of genomic DNA. Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into the genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Frommer et al, 1992, Proc. Nat. Acad, Sci. USA, 89, 1.827-1830. PCR amplification of the bisulfite converted DNA is then performed using primers specific for the CpG sites of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection using specific, labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR product in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, this technique can be reliably applied to DNA obtained from micro-dissected paraffin-embedded tissue samples. Typical reagents (e.g., as might be found in a typical COBRA-based kit) for COBRA analysis may include, but are not limited to: PCR primers for specific gene (or methylation altered DNA sequence or CpG island); restriction enzyme and appropriate buffer; gene-hybridization oligo; control hybridization oligo; kinase labeling kit for oligo probe; and radioactive nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

In an embodiment, the methylation profile of selected CpG sites is determined using methylation-Specific PCR (MSP). MSP allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al, 1996, Proc. Nat. Acad. Sci. USA, 93, 9821-9826; U.S. Pat. Nos. 5,786,146, 6,017,704, 6,200,756, 6,265,171 (Herman and Baylin); U.S. Pat. Pub. No. 2010/0144836 (Van Engeland et al); which are hereby incorporated by reference in their entirety). Briefly, DNA is modified h a deaminating agent such as sodium bisulfite to convert unmethylated, but not methylated cytosines to uracil, and subsequently amplified with primers specific for methylated versus unmethylated DNA. In some instances, typical reagents (e.g., as might be found in a typical MSP-based kit) for MSP analysis include, but are not limited to: methylated and unmethylated PCR primers for specific gene (or methylation-altered DNA sequence or CpG island), optimized PCR buffers and deoxynucleotides, and specific probes. One may use quantitative multiplexed methylation specific PCR (QM-PCR), as described by Fackler et al. Fackler et al, 2004, Cancer Res. 64(13) 4442-1152; or Fackler et al, 2005. Clin. Cancer Res. 12(11 Pt 1) 3306-3310.

In an embodiment, the methylation profile of selected CpG sites is determined using MethyLight and/or Heavy Methyl Methods. The MethyLight and Heavy Methyl assays are a high-throughput quantitative methylation assay that utilizes fluorescence-based real-time PCR (Taq Man®) technology that requires no further manipulations after the PCR step (Eads, C. A. et al, 2000, Nucleic Acid Res. 28, e 32; Cottrell et a 2007, J. Urology 177, 1753. U.S. Pat. No. 6,331,393 (Laird et al), the contents of which are hereby incorporated by reference in their entirety). Briefly, the MethyLight process begins with a mixed sample of genomic DNA that is converted, in a sodium bisulfite reaction, to a mixed pool of methylation-dependent sequence differences according to standard procedures (the bisulfite process converts unmethylated cytosine residues to uracil). Fluorescence-based PCR is then performed either in an “unbiased” (with primers that do not overlap known CpG methylation sites) PCR reaction, or in a “biased” (with PCR primers that overlap known CpG dinucleotides) reaction. In some cases, sequence discrimination occurs either at the level of the amplification process or at the level of the fluorescence detection process, or both. In some cases, the MethyLight assay is used as a quantitative test for methylation patterns in the genomic DNA sample, wherein sequence discrimination occurs at the level of probe hybridization. In this quantitative version, the PCR reaction provides for unbiased amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe overlie any CpG dinucleotides. Alternatively, a qualitative rest for genomic methylation is achieved by probing of the biased PCR pool with either control oligonucleotides that do not “cover” known methylation sites (a fluorescence-based version of the “MSP” technique), or with oligonucleotides covering potential methylation sites. Typical reagents (e.g., as might be found in a typical MethyLight-based kit) for MethyLight analysis may include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG Island); TaqMan® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase.

Quantitative MethyLight uses bisulfite to convert genomic DNA and the methylated sires are amplified using PCR with methylation independent primers. Detection probes specific for the methylated and unmethylated sites with two different fluorophores provides simultaneous quantitative measurement of the methylation. The Heavy Methyl technique begins with bisulfate conversion of DNA. Next specific blockers prevent the amplification of unmethylated DNA. Methylated genomic DNA does not bind the blockers and their sequences will be amplified. The amplified sequences are detected with a methylation specific probe. (Cottrell et al, 2004, Nuc. Acids Res. 32:e10, the contents of which is hereby incorporated by reference in its entirety).

The Ms-SNuPE technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo and Jones, 1997, Nucleic Acids Res. 25, 2529-2533). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site(s) of interest. In some cases, small amounts of DNA are analyzed (e.g., micro-dissected pathology sections), and the method avoids utilization of restriction enzymes for determining the methylation status at CpG sites. Typical reagents (e.g., as is found in a typical Ms-SNuPE-based kit) for Ms-SNuPE analysis include, hut are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for specific gene; reaction buffer (for the Ms-SNuPE reaction); and radioactive nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery regents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

In another embodiment, the methylation status of selected CpG sites is determined using differential Binding-based Methylation Detection Methods. For identification of differentially methylated regions, one approach is to capture methylated DNA. This approach uses a protein, in which the methyl binding domain of MBD2 is fused to the Fc fragment of an antibody (MBD-FC) (Gebhard et al, 2006, Cancer Res. 66:6118-6128; and PCT Pub. No. WO 2006/056480 A2 (Relhi), the contents of which are hereby incorporated by reference in their entirety). This fusion protein has several advantages over conventional methylation specific antibodies. The MBD FC has a higher affinity to methylated DNA and it binds double stranded DNA. Most importantly the two proteins differ in the way they bind DNA. Methylation specific antibodies bind DNA stochastically, which means that only a binary answer can be obtained. The methyl binding domain of MBD-FC, on the other hand, binds DNA molecules regardless of their methylation status. The strength of this protein-DNA interaction is defined by the level of DNA methylation. After binding genomic DNA, eluate solutions of increasing salt concentrations can be used to fractionate non-methylated and methylated DNA allowing for a more controlled separation (Gebhard et al, 2006, Nucleic Acids Res. 34: e82). Consequently this method, called Methyl CpG immunoprecipitation (MCIP), not only enriches, but also fractionates genomic DNA according to methylation level, which is particularly helpful when the unmethylated DNA fraction should be investigated as well.

In an alternative embodiment, a 5-methyl cytidine antibody to bind and precipitate methylated DNA. Antibodies are available from Abeam (Cambridge, Mass.), Diagenode (Sparta, N.J.) or Eurogentec (c/o AnaSpec, Fremont, Calif.). Once the methylated fragments have been separated they may be sequenced using microarray based techniques such as methylated CpG-island recovery assay (MIRA) or methylated DNA immunoprecipitation (MeDIP) (Pelizzola et al, 2008, Genome Res. 18, 1652-1659; O'Geen et al, 2006, BioTechniques 41(5), 577-580. Weber et al, 2005, Nat. Genet. 37, 853-862: Horak and Snyder, 2002, Methods Enzymol. 350, 469-83; Lieb, 2003, Methods Mol Biol, 224, 99-109). Another technique is methyl-CpG binding domain column/segregation of partly melted molecules (MBD/SPM, Shiraishi et al, 1999, Proc. Natl. Acad. Sci. USA 96(0):2913-2918).

In some embodiments, methods for detecting methylation include randomly shearing or randomly fragmenting the genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme and subsequently selectively identifying and/or analyzing the cut or uncut DNA. Selective identification can include, for example, separating cut and uncut DNA (e.g., by size) and quantifying a sequence of interest that was cut or, alternatively, that was not cut. See, e.g., U.S. Pat. No. 7,186,512, Alternatively, the method can encompass amplifying intact DNA after restriction enzyme digestion, thereby only amplifying DNA that was not cleaved by the restriction enzyme in the area amplified, See, U.S. Pat. Nos. 7,910,296; 7,901,880; and 7,459,274. In some embodiments, amplification can be performed using primers that are gene specific.

For example, there are methyl-sensitive enzymes that preferentially or substantially cleave or digest at their DNA recognition sequence if it is non-methylated. Thus, an unmethylated DNA sample is cut into smaller fragments than a methylated DNA sample. Similarly, a hypermethylated DNA sample is not cleaved. In contrast, there are methyl-sensitive enzymes that cleave at their DNA recognition sequence only if it is methylated. Methyl-sensitive enzymes that digest unmethylated DNA suitable for use in methods of the technology include, but are not limited to, Hpall, Hhal, Maell, BstUI and Acil. In some instances, an enzyme that is used is Hpall that cuts only the unmethylated sequence CCGG. In other instances, another enzyme that is used is Hhal that cuts only the unmethylated sequence GCGC. Both enzymes are available from New England Biolabs®, Inc. Combinations of two or more methyl-sensitive enzymes that digest only unmethylated DNA are also used. Suitable enzymes that digest only methylated DNA include, but are not limited to, Dpnl, which only cuts at fully methylated 5′-GATC sequences, and McrBC, endonuclease, which cuts DNA containing modified cytosines (5-methylcytosine or 5-hydroxymethylcytosine or N4-methylcytosine) and cuts at recognition site 5′ . . . PumC(N4o-3ooo) PumC . . . 3′ (New England BioLabs, Inc., Beverly, Mass.). Cleavage methods and procedures for selected restriction enzymes for cutting DNA at specific sites are well known to the skilled artisan. For example, many suppliers of restriction enzymes provide information on conditions and types of DNA sequences cut by specific restriction enzymes, including New England BioLabs, Pro-Mega Biochems, Boehringer-Mannheim, and the like. Sambrook et al, (See Sambrook et al, Molecular Biology: A Laboratory Approach, Cold Spring Harbor, N.Y. 1989) provide a general description of methods for using restriction enzymes and other enzymes.

In some instances, a methylation-dependent restriction enzyme is a restriction enzyme that cleaves or digests DNA at or in proximity to a methylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is not methylated. Methylation-dependent restriction enzymes include those that cut at a methylated recognition sequence (Dpnl) and enzymes that cut at a sequence near but not at the recognition sequence (e.g., McrBC), For example. McrBC's recognition sequence is 5′ RmC (N40-3000) RmC 3′ where “R” is a purine and “mC” is a methylated cytosine and “N40-3000” indicates the distance between the two RmC half sites for which a restriction event has been observed. McrBC generally cuts close to one half-site or the other, but cleavage positions are typically distributed over several base pairs, approximately 30 base pairs from the methylated base. McrBC sometimes cuts 3′ of both half sites, sometimes 5′ of both half sites, and sometimes between the two sites. Exemplary methylation-dependent restrict on enzymes include, e.g., McrBC, McrA, MrrA, Bisl, Glal and Dpnl. One of skill in the air will appreciate that any methylation-dependent restriction enzyme, including homologs and orthologs, of the restriction enzymes described herein, is also suitable for use with one or more methods described herein.

In some cases, a methylation-sensitive restriction enzyme is a restriction enzyme that cleaves DNA at or in proximity to an unmethylated recognition sequence but does not cleave at or in proximity to the same sequence when the recognition sequence is methylated. Exemplary methylation-sensitive restriction enzymes are described in, e.g., McClelland et al, 22(1.7) NUCLEIC ACIDS RES. 3640-59 (1994). Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when a cytosine within the recognition sequence is methylated at position C5 include, e.g., Aat II, Aci I, Acd I, Age I, Alu I, Asc I, Ase I, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BmF I, BssH II, BssK I, BstB I, BstN I, BstU I, Cla I, Eae I, Eag I, Fau I, Fse I, Hha I, HinPl I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position NO include. e.g., Mbo I. One of skill in the art will appreciate that any methylation-sensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use with one or more of the methods described herein. One of skill in the art will further appreciate that a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of a cytosine at or near its recognition sequence may be insensitive to the presence of methylation of an adenosine at or near its recognition sequence. Likewise, a methylation-sensitive restriction enzyme that fails to cut in the presence of it of an adenosine at or near its recognition sequence may be insensitive to the presence of methylation of a cytosine at or near its recognition sequence. For example, suitable sensitive (i.e. fails to cut) to the presence of a methylated cytosine at or near its recognition sequence, but is insensitive (i.e., cuts) to the presence of a methylated adenosine at or near its recognition sequence. One of skill in the art will also appreciate that some methylation-sensitive restriction enzymes are blocked by methylation of bases on one or both strands of DNA encompassing of their recognition sequence, while other methylation-sensitive restriction enzymes are blocked only by methylation on both strands, but can cut if a recognition site is hemi-methylated.

In alternative embodiments, adaptors are optionally added to the ends of the randomly fragmented DNA, the DNA is then digested with a methylation-dependent of methylation-sensitive restriction enzyme, and intact DNA is subsequently amplified using primers that hybridize to the adaptor sequences. In this case, a second step is performed to determine the presence, absence or quantity of a particular gene in an amplified pool of DNA. In some embodiments, the DNA is amplified using real-time, quantitative PCR.

In other embodiments, the methods comprise quantifying the average methylation density m target sequence within a population of genomic DNA. In some embodiments, the method comprises contacting genomic DNA with a methylation-dependent restriction enzyme or methylation-sensitive restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved; quantifying intact copies of the locus; and comparing the quantity of amplified product to a control value representing the quantity of methylation of control DNA, thereby quantifying the average methylation density in the locus compared to the methylation density of the control DNA.

In some instances, the quantity of methylation of a locus of DNA is determined by providing, a sample of genomic DNA comprising the locus, cleaving the DNA with a restriction enzyme that is either methylation-sensitive or methylation-dependent, and then quantifying the amount of intact DNA or quantifying the amount of cut DNA at the DNA locus of interest. The amount of intact or cut DNA will depend on the initial amount of genomic DNA containing the locus, the amount of methylation in the locus, and the number (i.e., the fraction) of nucleotides in the locus that are methylated in the genomic DNA. The amount of methylation in a DNA locus can be determined by comparing the quantity of intact DNA or cut DNA to a control value representing the quantity of intact DNA or cut DNA in a similarly-treated DNA sample. The control value can represent a known or predicted number of methylated nucleotides. Alternatively, the control value can represent the quantity of intact or cut DNA from the same locus in another (e.g., normal, non-diseased) cell or a second locus.

By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow for at least scone copies at potential restriction enzyme cleavage sites in the locus to remain uncleaved and subsequently quantifying the remaining intact copies and comparing the quantity to a control, average methylation density of a locus can be determined. If the methylation-sensitive restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be directly proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Similarly, if a methylation-dependent restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved then the remaining intact DNA will be inversely proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Such assays are disclosed in, e.g., U.S. Pat. No. 7,010,296.

The methylated CpG island amplification (MCA) technique is a method that can be used to screen for altered methylation patterns in genomic. DNA, and to isolate specific sequences associated with these changes (Toyota et al, 1.999, Cancer Res, 59, 2307-2312, U.S. Pat. No. 7,700,324 (Issa et at), the contents of which are hereby incorporated by reference in their entirety). Briefly, restriction enzymes with different sensitivities to cytosine methylation in their recognition sites are used to digest genomic DNAs from primary tumors, canines, and normal tissues prior to arbitrarily primed PCR amplification. Fragments that show differential methylation are cloned and sequenced after resolving the PCR products on high-resolution polyacrylamide gels. The cloned fragments are then used as probes for Southern analysis to confirm differential methylation of these regions. Typical reagents (e.g., as might be found in a typical MCA-based kit) for MCA analysis may include, but are not limited to: PCR primers for arbitrary priming Genomic DNA; PCR buffers and nucleotides, restriction enzymes and appropriate buffers; gene-hybridization oligos or probes; control hybridization oligos or probes.

Additional methylation detection methods include those methods described in, e.g., U.S. Pat. Nos. 7,553,627; 6,331,393; U.S. patent Ser. No. 12/476,981; U.S. Patent Publication No. 2005/0069879; Rein, et al, 26(10) NUCLEIC ACIDS RES. 2255-64 (1998); and Olek et al, 17(3) NAT. GENET. 275-6 (1997).

In another embodiment, the methylation status of selected CpG sites is determined using Methylation-Sensitive High Resolution Melting (HRM). Recently, Wojdacz et al reported methylation-sensitive high resolution melting as a technique to assess methylation (Wojdacz and Dobrovic, 2007, Nuc. Acids Res., 35(6) e41l Wojdacz et al, 2008, Nat. Prot. 3(12) 003-1908; Balic et al., 2009 J. Mol. Diagn. 11 102-108; and US Pat. Pub. No. 2009/0155791 (Wojdacz et al), the contents of which are hereby incorporated by reference in their entirety). A variety of commercially available real time PCR machines have HRM systems including the Roche LightCycler480, Corbett Research RotorGene6000, and the Applied Biosystems 7500. HRM may also be combined with other amplification techniques such pyrosequencing as described by Candiloro et al. (Candiloro et al, 2011, Epigenetics 6(4) 500-507).

In another embodiment, the methylation status of selected CpG locus is determined using a primer extension assay, including an optimized PCR amplification reaction that produces amplified targets for analysis using mass spectrometry. The assay can also be done in multiplex. Mass spectrometry is a particularly effective method for the detection of polynucleotides associated with the differentially methylated regulatory elements. The presence of the polynucleotide sequence is verified by comparing the mass of the detected signal with the expected mass of the polynucleotide of interest. The relative signal strength, e.g., mass peak on a spectra, for a particular polynucleotide sequence indicates the relative population of a specific allele, thus enabling calculation of the allele ratio directly from the data. This method is described in detail in PCT Pub. No. WO 2005/012578A1 (Beaulieu et al), which is hereby incorporated by reference in its entirely. For methylation analysis, the assay can be adopted to detect bisulfite introduced methylation dependent C to T sequence changes. These methods are particularly useful for performing multiplexed amplification reactions and multiplexed primer extension reactions (e.g., multiplexed homogeneous primer mass extension (hME) assays) in a single well to further increase the throughput and reduce the cost per reaction for primer extension reactions.

Other methods for DNA methylation analysis include restriction landmark genomic scanning (RLGS, Costello et al, 2002, Meth. Mol Biol, 200, 53-70), methylation-sensitive-representational difference analysis (MS-RDA, Ushijima and Yamashita, 2009, Methods Mol Biol 507, 1 17-130). Comprehensive high-throughput arrays for relative methylation (CHARM) techniques are described in WO 2009/021141 (Feinberg and Irizarry). The Roche® NimbleGen® microarrays including the Chromatin Immunoprecipitation-on-chip (ChIP-chip) or methylated DNA immunoprecipitation-on-chip (MeDIP-chip). These tools have been used for a variety of cancer applications including melanoma, liver cancer and lung cancer (Koga et al, 2009, Genome Res., 19.3462-1470; Acevedo et al, 2008, Cancer Res., 68, 2641-2651; Rauch et al, 2008, Proc. Nat. Acad. Sci. USA. 105, 252-257). Others have reported bisulfate conversion, padlock probe hybridization, circularization, amplification and next generation or multiplexed sequencing for high throughput detection of methylation (Deng et al, 2009, Nat. Biotechnol 27, 353-360; Ball et al, 2009, Nat. Biotechnol 27, 361-368; U.S. Pat. No. 7,611,869 (Fan)). As an alternative to bisulfate oxidation. Bayeyt et al. have reported selective oxidants that oxidize 5-methylcytosine, without reacting with thymidine, which are followed by PCR or pyro sequencing (WO 2009/049916 (Bayeyt et al). These references for these techniques are hereby incorporated by reference in their entirety.

In some instances, quantitative amplification methods (e.g., quantitative PCR or quantitative linear amplification) are used to quantify the amount of intact DNA within a locus flanked by amplification primers following restriction digestion. Methods of quantitative amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972.602, as well as in, e.g., DeGraves, et al, 34(1) BIOTECHNIQUES 106-15 (2003); Deiman B. et al., 20(2) MOL. BIOTECHNOL. 163-79 (2002); and Gibson et al, 6 GENOME RESEARCH 995-1001 (1996).

Following reaction or separation of nucleic acid in a methylation specific manner, the nucleic acid in some cases are subjected to sequence-based analysis. For example, once it is determined that one particular genomic sequence from a sample is hyper methylated or hypomethylated compared to its counterpart, the amount of this genomic sequence can be determined. Subsequently, this amount can be compared to a standard control value and used to determine the present of liver cancer in the sample. In many instances, it is desirable to amplify a nucleic acid sequence using any of several nucleic acid amplification procedures which are well known in the art. Specifically, nucleic acid amplification is the chemical or enzymatic synthesis of nucleic acid copies which contain a sequence that is complementary to a nucleic acid sequence being amplified (template). The methods and kits may use any nucleic acid amplification or detection methods known to one skilled in the art, such as those described in U.S. Pat. No. 5,525,462 (Takarada et al); U.S. Pat. No. 6,114,117 (Hepp et al); U.S. Pat. No. 6,127,120 (Graham et al); U.S. Pat. No. 6,344,317 (Urnovitz); U.S. Pat. No. 6,448,001 (Oku); U.S. Pat. No. 6,528,632 (Catanzariti et al); and PCT Pub. No. WO 2005/111209 (Nakajima et al); all of which are incorporated herein by reference in their entirety.

In some embodiments, the nucleic acids are amplified by PCR amplification using methodologies known to one skilled in the art. One skilled in the art wilt recognize, however, that amplification can be accomplished by any known method, such as ligase chain reaction (LCR), Q-replicas amplification, rolling circle amplification, transcription amplification, self-sustained sequence replication, nucleic acid sequence based amplification (NASBA), each of which provides sufficient amplification. Branched-DNA technology is also optionally used to qualitatively demonstrate the presence of a sequence of the technology, which represents a particular methylation pattern, or to quantitatively determine the amount of this particular genomic sequence in a sample. Nolte reviews branched-DNA signal amplification for direct quantitation of nucleic acid sequences in clinical samples (Nolte, 1998. Adv. Clin. Chem. 33:201-235).

The PCR process is well known in the art and include, for example, reverse transcription PCR, ligation mediated PCR, digital PCR (dPCR), or droplet digital PCR (ddPCR). For a review of PCR methods and protocols, see, e.g., Innis et al. eds., PCR Protocols, A Guide to Methods and Application, Academic Press, Inc., San Diego. Calif. 1990; U.S. Pat. No. 4,683,202 (Mullis). PCR reagents and protocols are also available from commercial vendors, such as Roche Molecular Systems. In some instances, PCR is carried out as an automated process with a thermostable enzyme. In this process, the temperature of the reaction mixture is cycled through a denaturing region, a primer annealing region, and an extension reaction region automatically. Machines specifically adapted for this purpose are commercially available.

In some embodiments, amplified sequences are also measured using invasive cleavage reactions such as the Invader® technology (Zou et al, 2010, Association of Clinical Chemistry (AACC) poster presentation on Jul. 28, 2010. “Sensitive Quantification of Methylated Markers with a Novel Methylation Specific Technology; and U.S. Pat. No. 7,011,944 (Prudent et al)).

Suitable next generation sequencing technologies are widely available. Examples include the 454 Life Sciences platform (Roche, Branford, Conn.) (Margulies et al, 2005 Nature, 437, 376-380); lllumina's Genome Analyzer, GoldenGate Methylation Assay, or Infinium Methylation Assays, i.e., Infinium HumanMethylation 27K Bead Array or VeraCode GoldenGate methylation array (Illumina, San Diego, Calif.; Bibkova et al 2006, Genome Res. 16, 383-393; U.S. Pat. Nos. 6,306,597 and 7,598,035 (Macevicz); U.S. Pat. No. 7,232,656 (Balasubramanian et al.)): QX200™ Droplet Digital™ PCR System from Bio-Rad; or DNA Sequencing by Ligation, SOLiD System (Applied Biosystems/Life Technologies; U.S. Pat. Nos. 6,797,470, 7,083,917, 7,166,434, 7,320,865, 7,332,285, 7,364,858, and 7,429,453 (Barany et at); the Helicos True Single Molecule DNA sequencing technology (Harris et al, 2008 Science, 320, 106-109; U.S. Pat. Nos. 7,037,687 and 7,645,596 (Williams et al); U.S. Pat. No. 7,169,560 (Lapidus et al); U.S. Pat. No. 7,769,400 (Hanis)), the single molecule, real-time (SMRT™) technology of Pacific Biosciences, and sequencing (Soni and Meller, 2007, Clin. Chem. 53, 1996-2001); semiconductor sequencing (Ion Torrent; Personal Genome Machine); DNA nanoball sequencing; sequencing using technology from Dover Systems (Polonator), and technologies that do not require amplification or otherwise transform native DNA prior to sequencing (e.g., Pacific Biosciences and Helicos), such as nanopore-based strategies (e.g., Oxford Nanopore, Genia Technologies, and Nabsys). These systems allow the sequencing of many nucleic acid molecules isolated from a specimen at high orders of multiplexing in a parallel fashion. Each of these platforms allow sequencing of clonally expanded or non-amplified single molecules of nucleic acid fragments. Certain platforms involve, for example, (i) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (ii) pyrosequencing, and (iii) single-molecule sequencing.

Pyrosequencing is a nucleic acid sequencing method based on sequencing by synthesis, which relics on detection of u pyrophosphate released on nucleotide incorporation. Generally, sequencing by synthesis involves synthesizing, one nucleotide at a time, a DNA strand complimentary to the strand whose sequence is being sought. Study nucleic acids may be immobilized to a solid support, hybridized with a sequencing primer, incubated with DNA polymerase, ATP sulfurylase, luciferase, apyrase, adenosine 5′ phosphosulfate and luciferin. Nucleotide solutions are sequentially added and removed. Correct incorporation of a nucleotide releases a pyrophosphate, which interacts with ATP sulfurylase and produces ATP in the presence of adenosine 5′ phosphosulfate, fueling the luciferin reaction, which produces a chemiluminescent signal allowing sequence determination. Machines for pyrosequencing and methylation specific reagents are available from Qiagen, Inc. (Valencia, Calif.). See also Tost and Gut, 2007, Nat. Prot. 2 2265-2275. An example of a system that can be used by a person of ordinary skill based on pyrosequencing generally involves the following steps: ligating an adaptor nucleic acid to a study nucleic acid and hybridizing the study nucleic acid to a bead; amplifying u nucleotide sequence in the study nucleic acid in an emulsion; sorting beads using a picoliter multi well solid support: and sequencing amplified nucleotide sequences by pyrosequencing methodology (e.g., Nakano et al, 2003, J. Biotech. 102, 117-124). Such a system can be used to exponentially amplify amplification products generated by a process described herein, e.g., by ligating a heterologous nucleic acid to the first amplification product generated by a process described herein.

CpG Methylation Data Analysis Methods

In certain embodiments, the methylation values measured for biomarkers of a biomarker panel are mathematically combined and the combined value is correlated to the underlying diagnostic question. In some instances, methylated biomarker values are combined by any appropriate state of the art mathematical method. Well-known mathematical methods for correlating a biomarker combination to a disease status employ methods like discriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA), Discriminant Functional Analysis (DFA), Kernel Methods (e.g., SVM), Multidimensional Scaling (MDS), Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting/Bagging Methods), Generalized Linear Models (e.g., Logistic Regression), Principal Components based Methods (e.g., SIMCA), Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based Methods. The skilled artisan will have no problem in selecting an appropriate method to evaluate an epigenetic marker or biomarker combination described herein. In one embodiment, the method used in a correlating methylation status of an epigenetic marker or biomarker combination, e.g. to diagnose liver cancer or a liver cancer subtype, is selected from DA (e.g., Linear-, Quadratic-, Regularized Discriminant Analysis), DFA, Kernel Methods (e.g., SVM), MDS, Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Basal Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting Methods), or Generalized Linear Models (e.g., Logistic Regression), and Principal Components Analysts. Details relating to these statistical methods are found in the following references: Ruczinski et al., 12 J. OF COMPUTATIONAL AND GRAPHICAL STATISTICS 475-511 (2003); Friedman, J. H., 84 J. OF THE AMERICAN STATISTICAL ASSOCIATION 165-75 (1989); Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, The Elements of Statistical Learning, Springer Series in Statistics (2001); Breiman, L., Friedman, J. H., Olshen. R. A., Stone. C. J. Classification and regression trees, California; Wadsworth (1984); Breiman, L., 45 MACHINE LEARNING 5-32 (2001); Pepe. M. S., The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford Statistical Science Series, 28 (2003); and Duda, R. O., Hart, P. E., Stork. D. O., Pattern Classification, Wiley Interscience, 2nd Edition (2001).

In one embodiment, the correlated results for each methylation panel are rated by their correlation to the disease or tumor type positive state, such as for example, by p-value test or t-value test or F-test. Rated (best first, i.e. low p- or t-value) biomarkers are then subsequently selected and added to the methylation panel until a certain diagnostic value is reached. Such methods include identification of methylation panels, or more broadly, genes that were differentially methylated among several classes using, for example, a random-variance t-test (Wright G. W, and Simon R. Bioinformatics 19:2448-2455, 2003). Other methods include the step of specifying a significance level to be used for determining the epigenetic markers that will be included in the biomarker panel. Epigenetic markers that are differentially methylated between the classes at a univariate parametric significance level less than the specified threshold are included in the panel, it doesn't matter whether the specified significance level is small enough to exclude enough false discoveries. In some problems better prediction is achieved by being more liberal about the biomarker panels used as features. In some cases, the panels are biologically interpretable and clinically applicable, however, if fewer markers are included. Similar to cross-validation, biomarker selection is repeated for each training set created in the cross-validation process. That is for the purpose of providing ail unbiased estimate of prediction error. The methylation panel for use with new patient sample data is the one resulting from application of the methylation selection and classifier of the “known” methylation information, or control methylation panel.

Models for utilizing methylation profile to predict the class of future samples can also be used. These models may be based on the Compound Covariate Predictor (Radmacher et al. Journal of Computational Biology 9:505-511, 2002), Diagonal Linear Discriminant Analysis (Dudoit et al. Journal of the American Statistical Association 97:77-87, 2002). Nearest Neighbor Classification (also Dudoit et al.), and Support Vector Machines with linear kernel (Ramaswamy et al. PNAS USA 98:15149-54, 2001). The models incorporated markers that were differentially methylated at a given significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W, and Simon R. Bioinformatics 19:2448-2455, 2003). The prediction error of each model using cross validation, preferably leave-one-out cross-validation (Simon et al. Journal of the National Cancer Institute 95:14-18, 2003 can be estimated. For each leave-one-out cross-validation training set, the entire model building process is repeated, including the epigenetic marker selection process. In some instances, it is also evaluated in whether the cross-validated error rate estimate for a model is significantly less than one would expect from random prediction. In some cases, the class labels are randomly permuted and the entire leave-one-out cross-validation process is then repeated. The significance level is the proportion of the random permutations that gives a cross-validated error rate no greater than the cross-validated error rate obtained with the real methylation data.

Another classification method is the greedy-pairs method described by Bo and Jonassen (Genome Biology 3(4):research0017.1-0017.11, 2002). The greedy-pairs approach starts with ranking all markers based on their individual t-scores on the training set. This method attempts to select pairs of markers that work well together to discriminate the classes.

Furthermore, a binary tree classifier for utilizing methylation profile is optionally used to predict the class of future samples. The first node of the tree incorporated a binary classifier that distinguished two subsets of the total set of classes. The individual binary classifiers are based on the “Support Vector Machines” incorporating markers that were differentially expressed among markers at tire significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W, and Simon R. Bioinformatics 19:2448-2455, 2003). Classifiers for all possible binary partitions are evaluated and the partition selected is that for which the cross-validated prediction error is minimum. The process is then repeated successively for the two subsets of classes determined by the previous binary split. The prediction error of the binary tree classifier can be estimated by cross-validating the entire tree building process. This overall cross-validation includes re-selection of the optimal partitions at each node and re-selection of the markers used for each cross-validated training set as described by Simon et al. (Simon et al. Journal of the National Cancer Institute 95:14-18, 2003). Several-fold cross validation in which a fraction of the samples is withheld, a binary tree developed on the remaining samples, and then class membership is predicted for the samples withheld. This is repeated several times, each time withholding a different percentage of the samples. The samples are randomly partitioned into fractional test sets (Simon R and Lam A. BRB-ArrayTools User Guide, version 3.2. Biometric Research Branch, National Cancer Institute).

Thus, in one embodiment, the correlated results for each marker b) are rated by their correct correlation, to the disease, preferably by p-value test. It is also possible to include u step in that the markers are selected d) in order of their rating.

In additional embodiments, factors such as the value, level, feature, characteristic, property, etc. of a transcription rate, mRNA level, translation rate, protein level, biological activity, cellular characteristic or property, genotype, phenotype, etc. can be utilized in addition prior to, during, or after administering a therapy to a patient to enable further analysis of the patient's cancer status.

In some embodiments, a diagnostic test to correctly predict status is measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic (“ROC”) curve. In some instances, sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. In some cases, an ROC curve provides the sensitivity of a test as a function of 1-specificity. The greater the area under the ROC curve, for example, the more accurate or powerful the predictive value of the test. Other useful measures of the utility of a test include positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.

In some embodiments, one or more of the biomarkers disclosed herein show a statistical difference in different samples of at least p<0.05, p<10⁻², p<10⁻³, p<10⁻⁴ or p<10⁻⁵. Diagnostic tests that use these biomarkers may show an ROC of at least 0.6, at least about 0.7, at least about 0.8, or at least about 0.9. In some instances, the biomarkers are differentially methylated in different subjects with or without liver cancer. In additional instances, the biomarkers for different subtypes of liver cancer are differentially methylated. In certain embodiments, the biomarkers are measured in a patient sample using the methods described herein and compared, for example, to predefined biomarker levels and are used to determine whether the patient has liver cancer, which liver cancer subtype does the patient have, and/or what is the prognosis of the patient having liver cancer. In other embodiments, the correlation of a combination of biomarkers in a patient sample is compared, for example, to a predefined set of biomarkers. In some embodiments, the measurement(s) is then compared with a relevant diagnostic amounts), cut-off(s), or multivariate model scores that distinguish between the presence or absence of liver cancer, between liver cancer subtypes, and between a “good” or a “poor” prognosis. As is well understood in the art, by adjusting the particular diagnostic cut-off(s) used in an assay, one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician. In some embodiments, the particular diagnostic cut-off is determined, for example, by measuring the amount of biomarker hypermethylation or hypomethylation in a statistically significant number of samples from patients with or without liver cancer and from patients with different liver cancer subtypes, and drawing the cut-off to suit the desired levels of specificity and sensitivity.

Kits/Article of Manufacture

In some embodiments, provided herein include kits for detecting and/or characterizing the methylation profile of a biomarker described herein. In some instances, the kit comprises a plurality of primers or probes to detect or measure the methylation status/levels of one or more samples. Such kits comprise, in some instances, at least one polynucleotide that hybridizes to at least one of the methylation marker sequences described herein and at least one reagent for detection of gene methylation. Reagents for detection of methylation include, e.g., sodium bisulfate, polynucleotides designed to hybridize to sequence that is the product of a marker sequence if the marker sequence is not methylated (e.g., containing at least one C-U conversion), and/or a methylation-sensitive or methylation-dependent restriction enzyme. In some cases, the kits provide solid supports in the form of an assay apparatus that is adapted to use in the assay. In some instances, the kits further comprise detectable labels, optionally linked to a polynucleotide, e.g., a probe, in the kit.

In some embodiments, the kits comprise one or more (e.g., 1, 2, 3, 4, or more) different polynucleotides (e.g., primers and/or probes) capable of specifically amplifying at least a portion of a DNA region of a biomarker described herein. Optionally, one or more detectably-labeled polypeptides capable of hybridizing to the amplified portion are also included in the kit. In some embodiments, the kits comprise sufficient primers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions thereof, and optionally include detectably-labeled polynucleotides capable of hybridizing to each amplified DNA region or portion thereof. The kits further can comprise a methylation-dependent or methylation sensitive restriction enzyme and/or sodium bisulfite.

In some embodiments, the kits comprise sodium bisulfite, primers and adapters (e.g., oligonucleotides that can be ligated or otherwise linked to genomic fragments) for whole genome amplification, and polynucleotides (e.g., detectably-labeled polynucleotides) to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region of an epigenetic marker described herein.

In some embodiments, the kits comprise methylation sensing restriction enzymes (e.g., a methylation-dependent restriction enzyme and/or a methylation-sensitive restriction enzyme), primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region of an epigenetic marker described herein.

In some embodiments, the kits comprise a methylation binding moiety and one or more polynucleotides to quantify the number of copies of at least a portion of a DNA region of a marker described herein. A methylation binding moiety refers to a molecule (e.g., a polypeptide) that specifically binds to methyl-cytosine.

Examples include restriction enzymes or fragments thereof that lack DNA cutting activity but retain the ability to bind methylated DNA, antibodies that specifically bind to methylated DNA, etc.).

In some embodiments, the kit includes a packaging material. As used herein, the term “packaging material” can refer to a physical structure housing the components of the kit. In some instances, the packaging material maintains sterility of the kit components, and is made of material commonly used for such purposes (e.g., paper, corrugated fiber, glass, plastic, foil, ampules, etc.). Other materials useful in the performance of the assays are included in the kits, including test tubes, transfer pipettes, and the like. In some cases, the kits also include written instructions for the use of one or more of these reagents in any of the assays described herein.

In some embodiments, kits also include a buffering agent, a preservative, or a protein/nucleic acid stabilizing agent. In some cases, kits also include other components of a reaction mixture as described herein. For example, kits include one or more aliquots of thermostable DNA polymerase as described herein, and/or one or more aliquots of dNTPs. In some cases, kits also include control samples of known amounts of template DNA molecules harboring the individual alleles of a locus. In some embodiments, the kit includes a negative control sample, e.g., a sample that does not contain DNA molecules harboring the individual alleles of a locus. In some embodiments, the kit includes a positive control sample, e.g., a sample containing known amounts of one or more of the individual alleles of a locus.

Certain Terminology

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which the claimed subject matter belongs. It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of any subject matter claimed. In this application, the use of the singular includes the plural unless specifically stated otherwise, it must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. In this application, the use of “or” means “and/or” unless stated otherwise. Furthermore, use of the term “including” as well as other forms, such as “include”, “includes,” and “included” is not limiting.

As used herein, ranges and amounts can be expressed as “about” a particular value or range. About also includes the exact amount. Hence “about 5 μL” means “about 5 μL” and also “5 μL.” Generally, the term “about” includes an amount that would be expected to be within experimental error.

The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

As used herein, the terms “individual(s)”, “subject(s)” and “patient(s)” mean any mammal. In some embodiments, the mammal is a human. In some embodiments, the mammal is a non-human. None of the terms require or are limited to situations characterized by the supervision (e.g. constant or intermittent) of a health care worker (e.g. a doctor, a registered nurse, a nurse practitioner, a physician's assistant, an orderly or a hospice worker).

A “site” corresponds to a single site, which in some cases is a single base position or a group of correlated base positions, e.g., a CpG site. A “locus” corresponds to a region that includes multiple sites. In some instances, a locus includes one site.

EXAMPLES

These examples are provided for illustrative purposes only and not to limit the scope of the claims provided herein.

Example 1

Lung cancer (LUNC) and Hepatocellular carcinoma (HCC) are leading causes of cancer deaths worldwide. As with many cancers. LUNC and HCC found at an early stage carries much-improved prognosis compared to advanced stage disease, in part due to the relative efficacy of local treatments compared with systemic therapy. In some cases, early detection has the potential for reducing mortality.

DNA methylation is an epigenetic regulator of gene expression that usually results in gene silencing. In cancer, DNA methylation is typically increased in minor suppressor genes and presents itself as one of the first neoplastic changes. Circulating tumor DNA (ctDNA) comprises of extracellular nucleic acid fragments sited into plasma via rumor cell necrosis, apoptosis, and active release of DNA. In some cases, ctDNA bearing cancer-specific methylation patterns is used as a bio marker in diagnosis of cancers.

Patient Data

Tissue DNA methylation data was obtained from The Cancel Genome Atlas (TCGA). Complete clinical, molecular, and histopathological datasets are available at the TCGA website. Individual institutions that contributed samples coordinated the consent process and obtained informed written consent from each patient in accordance to their respective institutional review boards.

A second independent Chinese cohort consisted of 654 LUNC and 654 HCC patients at the Sun Yat-sen University Cancer Center in Guangzhou, Xijing Hospital in Xi'an and the West China Hospital in Chengdu, China. Patients who presented with LUNC and HCC front stage I-IV were selected and enrolled in this study. Patient characteristics and tumor features are summarized in Table S1. The TNM staging classification for LUNC and HCC is according to the 7^(th) edition of the AJCC cancer staging manual (Edge, S. B., and Compton, C. C. (2010). The American Joint Committee on Cancer: the 7^(th) edition of the AJCC cancer staging manual and the future of TNM Annals of surgical oncology 17, 1471-1474). This project was approved by the IRBs of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. Informed consent was obtained from all patients. Tumor and normal tissues were obtained as clinically indicated for patient care and were retained for this study. Human blood samples were collected by venipuncture and plasma samples were obtained by taking supernatant after centrifugation and stored at −80° C. before cfDNA extraction.

Data Sources

DNA methylation data of 485,000 sites generated using the Infinium 450K Methylation Array were obtained from the TCGA and a dataset generated from the study Hannum, G., et al (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates, Mol Cell 49>359-367 (GSE40279) in which DNA methylation profiles for HCC and blood were analyzed. IDAT format files of the methylation data were generated containing the ratio values of each scanned bead. Using the minfi package from Bioconductor, these data files were converted into a score, referred to as a Beta value. Methylation values of the Chinese cohort were obtained by targeted bisulfate sequencing using a molecular inversion probe and analyzed as described below.

Statistical Analysis

DNA Methylation Marker Pre-Selection for Diagnostic and Prognostic Analysis

A differential methylation analysis on TCGA data was performed using a “moderated t-statistics shrinking” approach and the p-value for each marker was then corrected by multiple resting by the Benjamini-Hochberg procedure to control FOR at a significance level of 0.05. The list was ranked by adjusted p-value and selected the top 1000 markers for designing padlock probes for differentiating cancer (both LUNC and HCC) versus normal samples and a separate group of 1000 markers for differentiating LUNC versus HCC (FIG. 1). About 1673 padlock probes were obtained that gave positive and specific PCR amplification signals and they were therefore used as capture probes in the subsequent experiments in cfDNA samples. cfDNA samples with low qualify or fewer than 30,000 reads per sample were also eliminated. About 2,173 cfDNA samples were included in our study (654 LUNC and 654 HCC blood samples and 865 normal blood samples). Methylated reads for each marker were defined as total unique methylated reads and methylation values for each marker were defined as the proportion of read counts with methylation divided by total read counts. For particular methylation markers with less than 20 unique reads, an imputed mean methylation value of HCC or normal healthy controls was used.

Building a Diagnostic Model

cfDNA Feature Construction and MCB

Padlock probes were designed to encompass 2000 CpG methylation sites found to be differentially methylated in comparison between both LUNC and HCC versus blood, and between LUNC and HCC. Capture and sequencing were performed in bisulfate-converted cfDNA samples. The concept of MCBs was used to merge proximal CpG markers into a MCB, resulting in a total of 888 MCBs. For each MCB, the MCB-specific methylation value was quantified as log 10 (total methylated read count+1), using the log transform to reduce outlier effects.

cfDNA-Based Diagnostic Classifier Construction

cfDNA sample data obtained from patients diagnosed with liver cancer (HCC), lung cancer (LUNC) and normal controls were divided into training and validation cohorts. Samples were excluded if they had less than 30,000 total unique reads. From the remaining samples with sufficient reads to ensure a good representation of MCBs, we selected 654 each of lung and liver cancer samples, and 865 healthy samples to ensure a balanced dataset. The full dataset was randomly split with a 2:1 ratio to form the training and validation cohorts.

MCBs that showed good methylation ranges across cfDNA samples were selected. Further, a two sample t-test was used to identify MCBs with the largest standardized absolute mean difference of methylated reads of an MCB between cancer samples (LUNC or HCC) versus normal controls samples. The top MCBs were chosen according to the ranked p-values that were still significant after correcting form multiple testing and constructed a diagnostic prediction model using top 100 markers. The panel of MCBs were aggregated into a composite score (cd-score) to perform a pairwise binary classification between LUNC and normal. HCC and normal, and LUNC and HCC. The composite score was generated from a multinomial logistic regression model using the top 50 MCBs per comparison.

The pro-treatment or initial methylation level was obtained at the initial diagnosis, and the post-treatment level was evaluated approximately 2 months after treatment, where the treatment referral to either chemotherapy or surgical resection of tumor. The primary endpoint (including response to treatment: progressive disease (PD), partial response (PR) and stable disease (SD)) were defined according to the RECIST guideline (Eisenhauer et al., 2009). For patients treated with surgical removal and no recurrence at time of evaluation, we assumed they had complete response (CR). The difference of cd-score distribution between clinical categories was examined by one-sided t-test as the cd-score was shown to be non-normally distributed using a Shapiro-Wilk Test.

Building a Predictive Model for Prognosis and Survival

the potential to use a combined prognosis score (cp-score) system was investigated based on total methylation reads in ctDNA for prediction of prognosis in LUNC and HCC in combination with clinical and demographic characteristics including age, gender, and AJCC stage. For each type of cancer, two thirds of the observations were randomly selected from the full dataset as the training cohort, and treated the rest as the validation cohort. Variable selection were conducted on the training cohort and built the composite score on the validation cohort. Within the training cohort, the “randomized lasso” scheme was adopted (Meinshausen, N., and Bühlmann, P. (2010) Stability selection. Journal of the Royal Statistical Society; Series B (Statistical Methodology) 72, 417-473) to reduce the sampling dependency to stabilize the variable selection in order to select biomarkers with a high confidence. The cohort with a 2:1 ratio was randomly divided. The variable selection procedure was initially conducted on the two-third of the training cohort and then used the remaining one-third cohort to conduct an internal validation test within the same training cohort. A variable selection procedure was conducted as following, First, a univariate Cox regression model was used to remove excessive noise and selected any biomarker with p-value less than or equal to 0.05 basal on the Wald test. Second, LASSO was implemented with an optimal tuning parameter determined by either the expected generalization error from the 10-fold cross validation or the information based criteria AIC/BIC, whichever yielded the highest ρ² (the proportion of explained randomness) with the selected biomarkers. The discriminatory power in Cox regression of die selected biomarkers was further evaluated on the internal validation cohort using the concordance probability (also known as C-index). In HCC, both ρ² (Median 0.773; quartile: 0.722-0.838) and C-index (Median: 0.768; quartile: 0.728-0.796) demonstrated the potential of biomarkers in an internal validation cohort. Similarly in LUNC, both ρ² (Median 0.737; quartile: 0.636-0.793) and C-index (0.614; quartile: 0.585-0.650) demonstrated potential clinical utility of biomarkers. The biomarkers presented in at least 30 out of 100 runs were aggregated, which resulted in 10 markers in HCC and 12 markers in LUNC (Table 3). To evaluate the predictability of each panel externally, a composite score was obtained for each patient in the validation cohort by multiplying the unbiased coefficient estimates from the Cox regression and the methylation reads. A Kaplan-Meier curve and log-rank test were generated using die dichotomized composite score, which formed a high-risk and low-risk group membership assignment according to its median. This segmentation was compatible with that formal by AJCC stage. Time-depended ROC was used to summarize the discrimination potential of the composite score, AJCC stage and the combination of two, with ROC curves varying as a function of time and accommodating censored data. Finally, a multivariate Cox regression model was fitted to assess the significance of potential risk factors. All hypothesis testing was two-sided with p-value <0.05 considered to be statistically significant. All the analysis was conducted in R version 3.2.3 with the following packages used: ‘glmnet’, ‘pROC’, ‘limma’, ‘survival’, ‘survivalROC’, ‘survcomp’.

Tumor DNA Extraction

Genomic DNA extraction from freshly frozen healthy or cancer tissues was performed with QIAamp DNA Mini Kit (Qiagen) according to manufacturer's recommendations. Roughly 0.5 mg of tissue was used to obtain on average 5 μg of genomic DNA. DNA was stored at −20° C., and analyzed within one week of preparation.

DNA Extraction from FFPE Samples

Genomic DNA from frozen FFPE samples was extracted using QIAamp DNA FFPE Tissue Kit with several modifications. DNA samples were stored at −20° C. for further analysis.

Cell-Five DNA Extraction from Plasma Samples

cfDNA extraction from 1.5 ml of plasma samples was performed with QIAamp cfDNA Kit (Qiagen) according to manufacturer's recommendations.

Bisulfite Conversion of Genomic DNA

About 10 ng of DNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer's protocol, Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfite conversion was >99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CH (non-CG) dinucleotides.

Determination of DNA Methylation Levels by Deep Sequencing of Bis-DNA Captured with Molecular-Inversion (Padlock) Probes

CpG markers whose methylation levels significantly differed in any of the comparisons between any cancer tissue and normal blood were used to design padlock probes for capture and sequencing of cfDNA. Padlock-capture of bis-DNA was based on the technique on published methods with modifications (Deng. J., Shoemaker, R., Xie, B., Gore. A., LcProust, E. M., Antosicwicz-Bourget. J., Egli, D., Maherali, N., Park. I. H., Yu, et al. (2009). Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nat Biotechnol 27, 353-360; Diep, D. Plongthongkum, N., Gore. A., Fung, H. L., Shoemaker. R., and Zhang. K. (2012). Library-free methylation sequencing with bisulfite padlock probes. Nat Methods 9, 270-272; Porreca, G. J., Zhang. K., Li, J. B., Xie, B., Austin, D., Vassallo, S. L., LeProust. E. M., Peck. B. J., Emig, C. J., Dahl P., et al. (2007). Multiplex amplification of large sets of human exons. Nat Methods 4, 931-936).

Probe Design and Synthesis

Padlock probes were designed using the ppDesigner software. The average length of the captured region was 100 bp, with the CpG marker located in the central portion of the captured region. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produced probes of equal length. A 6-bp unique molecular identifier (UMI) sequence was incorporated in probe design to allow for the identification of unique individual molecular capture events and accurate scoring of DNA methylation levels.

Probes were synthesized as separate oligonucleotides using standard commercial synthesis methods (ITD). For capture experiments, probes were mixed, in-vitro phosphorylated with 14 PNK (NEB) according to manufacturer's recommendations and purified using P-30 Micro Bio-Spin columns (Bio-Rad).

Bis-DNA Capture

About 10 ng of bisulfite-converted DNA was mixed with padlock probes in 20 μl reactions containing IX Ampligase buffer (Epicentre). To anneal probes to DNA, 30 second denaturation at 95° C. was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second. Hybridization was left to complete for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2 U of PfuTurboCx polymerase (Agilent), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in IX Ampligase buffer. After 5 hour incubation at 55° C., reactions were denatured for 2 minutes at 94° C. 5 μl of exonuclease mix (20 U of Exo I and 100 U of ExoIII, both from Epicentre) was added and single-stranded DNA degradation was carried out at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C.

Circular products of site-specific capture were amplified by PCR with concomitant barcoding of separate samples. Amplification was carried out using primers specific to Linker DNA within padlock probes, one of which contained specific 6 bp barcodes. Both primers contained Illumina next-generation sequencing adaptor sequences. PCR was done as follows: 1× Phusion Plash Master Mix, 3 μl of captured DNA and 200 nM primers, using the following cycle: 10 s @ 98° C., 8× of (1 s @ 98° C., 5 s @ 58° C., 10 s @ 72° C.), 25× of (1 s @ 98° C., 15 s @ 72° C.), 60 s @ 72° C. PCR reactions were mixed and the resulting library was size selected to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp) using Agencourt AMPure XP beads (Beckman Coulter). Purity of the libraries was verified by PCR using Illumina flowcell adaptor primers (P5 and P7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries we sequenced using MiSeq and HiSeq2500 systems (Illumina).

Optimization of Capture Coverage Uniformity

Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non efficient probes (60-65% of captured regions with coverage >0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at >0.2× of average coverage,

Sequencing Data Analysis

Mapping of sequencing reads was done using the software tool bisReadMapper with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script. Reads were on-the-fly convened as if all C were non-methylated and mapped to in-silico converted DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2. Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single, unique read. Methylation frequencies were calculated for all CpG dinucleotides contained within the regions captured by padlock probes by dividing the numbers of unique reads carrying a C at the interrogated position by the total number of reads covering the interrogated position.

Identification of Blocks of Correlated Methylation (BCM)

Pearson correlation coefficients between methylation frequencies of each pair of CpG markers separated by no more than 290 bp were calculated separately across 50 cfDNA samples from each of the two diagnostic categories, e.g. normal blood and HCC. A value of Pearson's r <0.5 was used to identify boundaries between adjacent markers with uncorrelated methylation. Markers not separated by a boundary were combined into Methylation Correlated Blocks (MCBs). This procedure identified a total of ˜1550 BCMs in arch diagnostic category within our padlock data, combining between 2 and 22 CpG positions in each block. Methylation frequencies for entire BCMs were calculated by summing up the numbers of Cs at all interrogated CpG positions within a BCM and dividing by the total number of C+Ts at those positions.

DNA Isolation and Digital Quantitative PCR

Tumor and corresponding plasma samples were obtained from patients undergoing surgical tumor resection; samples were frozen and preserved in at −80° C. until use. Isolation of DNA and RNA from samples was performed using AllPrep DNA/RNA Mini kit and a cfDNA extraction kit, respectively (Qiagen, Valencia, Calif.).

Patient and Sample Characteristics

Clinical characteristics and molecular DNA methylation profiles ware collected for 827 LUNC and 377 HCC tumor samples from The Cancer Genome Atlas (TCGA) and 754 normal samples from a dataset used in the methylation study on aging (GSE40279) (Hannum, G., et al. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49, 359-367). Two cohorts of patients were studied. The first cohort was from solid tumors from TCGA and the second cohort was from ctDNA samples from China. To study ctDNA in LUNC and HCC, plasma samples were obtained from 2,173 Chinese patients with HCC or LUNC and randomly selected population matched healthy controls undergoing routine health care maintenance, resulting in a cohort of 654 LUNC and 654 HCC patients and 865 normal healthy controls. All participants provided written informed consent. Clinical characteristics of all patients and controls are listed in table 4.

Identification of Methylation Markers Differentiating LUNC and HCC and Blood

It was hypothesized that since cfDNA originating from tumor cells is detected in a background of cfDNA predominantly released from leukocytes, CpG markers with a maximal difference in methylation values between LUNC or HCC versus normal leukocytes demonstrates detectable methylation differences in the cfDNA of HCC or LUNC patients when compared to that of normal controls. To identify putative markers, methylation data derived from cancer tissue DNA from the TCGA was compared with normal blood including 827 LUNC, 436 HCC, and 754 blood samples from healthy controls. In order to identify DNA sires with significantly different rates of methylation between LUNC or HCC and normal blood, a t-statistic with Empirical Bayes for shrinking the variance was used and fop 1000 markers were selected, using the Benjamini-Hochberg procedure to control the FDR at a significance level of 0.05. Unsupervised hierarchical clustering of these fop 1000 markers was able to distinguish between LUNC, HCC, and normal blood (FIG. 6). About 2,000 molecular inversion (padlock) probes corresponding to these 2000 markers for capture-sequencing cfDNA from plasma were designed.

Methylation Block Structure for Improved Allele Calling Accuracy

The well-established concept of generic linkage disequilibrium (LD block) (Reich, D. E., et al., (2001) Linkage disequilibrium in the human genome. Nature 411, 199-204) was employed to study the degree of co-methylation among different DNA strands, with the underlying assumption is that DNA sites in close proximity are more likely to be co-methylated than distant sites. Paired-end Illumina sequencing reads were used to identify each individual methylation block (mBlock), and applied a Pearson correlation method to quantify co-methylation or mBlock. All common mBlocks of a region were compiled by calculating different mBlock fractions. The genome is partitioned into blocks of tightly co-methylated CpG sites that we termed methylation correlated blocks (MCBs), using an r² cutoff of 0.5, usually employed in a genetic linkage disequilibrium analysis. MCBs were surveyed in ctDNA of 500 normal samples and found that MCBs are highly consistent. Methylation levels within an MCB ill cfDNA of 500 LUNC and 500 HCC samples were determined in addition to 500 normal samples. Similar to the case of a LD block, it was found that methylation pattern within an MCB was consistent when comparing normal versus HCC and LUNC cfDNA samples which significantly enhanced allele-calling accuracy (FIG. 7). Therefore, MCB values are used for all subsequent analyses. After filtering MCBs with a low dynamic methylation range to exclude non-informative MCBs (<5% in all cfDNA samples), 2000 differentially methylated CpG methylation sites were merged into 888 MCBs (FIG. 7). Also see Appendix A.

ctDNA Diagnostic Prediction Model for LUNC and HCC

The methylation values of the 888 selected MCBs that showed good methylation ranges in ctDNA samples (FIG. 7B) were analyzed by two-sample t-test to identify markers that gave the maximal standardized absolute mean difference of methylation reads for each marker between cancer samples (LUNC and HCC) versus normal controls samples. The top 100 most significant differentially methylated MCBs ranked by adjusted p-value after controlling the family-wise error rare were chosen to construct a diagnostic prediction model. Unsupervised hierarchical clustering of these MCBs by sample shown in FIG. 8A and methylated value of each selected MCB by tumor type shown in FIG. 9. For comparison, hierarchical clustering of all MCBs is shown in FIG. 8B. This panel of 100 MCB as used to generate a composite score (cd-score) in order to perform a pairwise binary classification between LUNC and normal, HCC and normal, and LUNC and HCC. The composite score was obtained from a multinomial logistic regression model using the top 50 MCBs per comparison. The full dataset was randomly split with a 2:1 ratio to form training and validation cohorts. The scoring system using 436 LUNC, 436 HCC and 576 normal control cfDNA samples were trained and then validated the score system in 218 LUNC, 218 HCC and 289 normal samples. Applying the model yielded a sensitivity of 85.8% for HCC and 80.3% for LUNC, and a specificity of 88.2% for in the validation dataset (Table 1 A). We also found that this model could successfully differentiate LUNC and HCC samples from normal controls in the validation cohort (AUC normal=0.957, AUC LUNC=0.937. AUC HCC=0.974) (FIG. 2A, Table 1B, Table 1C). Unsupervised hierarchical clustering of these 50 MCBs was able to distinguish HCC and LUNC from normal controls with high specificity and sensitivity (FIGS. 2C and 2D).

The cd-score of the model for differentiating between liver diseases (HBV/HCV infection, cirrhosis, and fatty liver) and HCC were assessed since these liver diseases are the known major risk factors for HCC. If was found that the cd-score was able to differentiate HCC patterns from those with liver diseases or healthy controls (FIG. 3A). These results were consistent and comparable with those predicted by AFP levels in HCC (FIG. 3B). The composite diagnostic score could also differentiate between LUNC patients and non-LUNC patients with a smoking history (>1 pack/day for ten years) who were at an increased risk of LUNC (FIG. 3C).

Methylation Markers Predicted Tumor Burden Treatment Response and Staging

The utility of the cd-score in assessing treatment response, the presence of residual rumor following treatment, and staging of LUNC and HCC were studied. In LUNC, The cd-scores of patients with detectable residual tumor following treatment (n=513) were significantly higher than those with no detectable tumor (n=126) (p=0.002, FIGS. 4A and 10B). Similarly, there was good correlation between the cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p=0.016, FIG. 4B). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=124) compared with those before surgery (n=59), yet became higher m patients with recurrence (n=48) (p<0.01, FIG. 3C). Furthermore, the cd-scores were significantly higher in patients before treatment (n=59) or with progression (n=120) compared to those with a positive treatment response (n=277) (p<0.001, FIG. 4D). In HCC, the cd-scores of patients with detectable residual tumor following treatment (n=495) were significantly higher than those with no detectable tumor (n=156(p<0.0001, FIG. 4E). Similarly, there was a highly positive correlation between cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p<0.001, FIG. 3F). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=146) compared with those before surgery (n=72), yet became higher in patients with recurrence (n=72) (p=0.036, FIG. 3G). Furthermore, the cd-scores were significantly higher in patients before treatment (n=72) or with progression (n=245) compared to those with treatment response (n=105) (p<0.001, FIG. 4H). We were able to obtain serial longitudinal dynamic changes of methylation values in several individuals with LUNC or HCC patient in order to monitor treatment response and found there was a high correlation between methylation values and treatment outcomes (FIGS. 10, 11 and 12). Collectively, these results suggest that the cd-score (i.e., the amount of ctDNA in plasma) is highly correlated with tumor but den and may have utility for predicting tumor response and for surveillance to detect recurrence.

Utility of ctDNA Diagnostic Prediction Model and AFP.

In some instances, the blood biomarker for risk assessment and surveillance of HCC is serum AFP levels. However, AFP has low sensitivity making it inadequate for detection of all patients that will develop HCC and severely limiting its clinical utility. In some cases, many cirrhotic patients develop HCC without any increase in AFP levels. For example, about 40% patients of the HCC study cohort have a normal AFP value (<25 ng/ml). In biopsy-proven HCC patients, the cd-score demonstrated superior sensitivity and specificity to AFP for HCC diagnosis (AUC 0.998 vs 0.855. FIG. 4I). Both cd-score and AFP values were highly correlated with tumor stage (FIGS. 4J and 4K). In patients with treatment response, tumor recurrence, or progression, cd-score showed more significant changes from initial diagnosis than that of AFP (FIGS. 4K and 4L). In patients with serial samples, those with a positive treatment response had a concomitant and significant decrease in cd-score compared to that prior to treatment, and there was an even further reduction in cd-score in patients after surgery. In contrast, patients with progressive or recurrent disease had an increase in methylation rate (FIG. 10). By comparison, AFP was less sensitive for assessing treatment efficacy in individual patients (FIG. 12).

ctDNA Prognostic Model for HCC and LUNC

The potential to use a combined prognosis score (cp-score) based on cfDNA methylation analysis for prediction of prognosis in LUNC and HCC in combination with clinical and demographic characteristics including age, gender, AJCC stage, anti AFP value was investigated in the HCC cohort, the training dataset contained 764 observations with 69 events and the validation dataset contained 382 observations with 31 events. By using statistical learning methods, a predictive model using 10 CpG markers (Table 3) that can separate the HCC cohort into high and low risk groups was constructed, with median survival significantly greater in the low-risk group than in the high-risk group (log-rank test=4.884, df=1, p=0.027) (FIG. 5A). In the LUNC cohort, the training dataset contained 437 observations with 69 events and the validation dataset contained 220 observations with 33 events. A panel of 12 CpG markers (Table 3) was able to divide the LUNC cohort into high and low risk groups, with median survival significantly greater in the low-risk group than in the high-risk group (log-rank test=4.35, df=1, p=0.037) (FIG. 5B).

Multivariate Cox regression model showed that the cp-score was significantly correlated with incidence of mortality in both HCC and LUNC. The cp-score was an independent risk factor of survival both in HCC and LUNC validation cohorts (Coef 0.37; p=0.03 in HCC; Coef: 0.28; p=0.0017 in LUNC; Table S2). Interestingly, AFP was no longer significant as a risk factor when cp-score and other clinical characteristics were taken into account in HCC (Table 5). As expected, TNM stage (as defined by the AJCC guideline) predicted the prognosis of patients both in HCC and LUNC (FIGS. 5C and 5D). The combination of cp-score and TNM staging significantly improved our ability to predict prognosis in both HCC (AUC 0.762) and LUNC cohort (AUC 0.740) (FIGS. 5E and 5F).

Table 1A shows a confusino table of multi-classification predicted diagnosis in the validation cohort.

VALIDATION COHORT HCC LUNC NORMAL Totals HCC 187 13 8 LUNC 22 175 26 Normal 9 30 255 Totals 218 218 289 725 Correct 187 175 255 617 Sensitivity (%) 85.8 80.3 Specificity (%) 88.2 85.1

Table 1B shows a confusion table of binary classification predicted diagnosis between normal and HCC in the validation cohort.

VALIDATION COHORT HCC NORMAL Totals HCC 207 13 Normal 11 276 Totals 218 289 507 Correct 207 276 483 Sensitivity (%) 94.9 Specificity (%) 95.5 95.3

Table 1C shows a elusion table of binary classification diagnosis between normal and LUNC in the validation cohort.

VALIDATION COHORT LUNC NORMAL Totals LUNC 189 26 Normal 29 263 Totals 218 289 507 Correct 189 263 452 Sensitivity (%) 86.7 Specificity (%) 91.0 89.2

Table 1D shows a contingency table of binary classification diagnosis between LUNC and HCC in validation cohort.

VALIDATION COHORT HCC LUNC Totals HCC 196 14 LUNC 22 204 Totals 218 218 436 Correct 196 204 400 Sensitivity (%) 89.9 Specificity (%) 93.6 91.7

Table 2 shows characteristics of 100 methylation markers and their coefficients in diagnosis.

feature logistic logistic logistic selection: regression regression regression diagnosis Marker t-stat coef: lihc coef: normal coef: nsclc 99 caner vs cancer  6-415285 −17.61364538 −0.236675214 0.010279099 0.023841014 10 caner vs cancer 17-39068  −23.95893113 −0.037590318 −0.576572433 −0.855315385 93 caner vs cancer 8-28799 −18.05697716 0.216408897 0.192819267 0.366347583 3 caner vs cancer 17-759561 −26.52474648 −1.367721168 −0.956150328 −0.054902768 85 caner vs cancer  1-1568670 −18.62705548 0.008670072 0.423911407 0.022632223 88 caner vs cancer 22-222885 −18.57400396 −0.326246574 −0.198542802 0.43976473 89 caner vs cancer  3-1138223 −18.55509702 −0.393347975 −1.396202888 −1.807959073 90 caner vs cancer  1-2035950 −18.51737266 0.115297952 −0.4031146 0.0238655 4 caner vs cancer 17-759562 −26.49964747 −0.788704497 −1.269985136 0.08324756 1 caner vs cancer 17-802603 −27.95631084 −0.044811111 −0.439876458 −0.823216834 83 caner vs cancer  1-2130901 −18.64276869 0.432663118 −0.629658794 −0.417586625 82 caner vs cancer 18-434054 −18.741526 −0.401160179 −0.515157305 −0.804483279 92 caner vs cancer  6-262504 −18.22430745 −0.24712752 −0.169911531 0.266218748 94 caner vs cancer  2-741531 −17.86310499 0.356517591 −0.15981238 −0.35043126 79 caner vs cancer  3-452092 −18.90554153 −0.554783023 −1.031814454 −0.2629371 78 caner vs cancer  10-1350729 −18.91807833 −0.01273024 −0.355980173 −0.178555972 77 caner vs cancer 22-455977 −18.94775481 1.345069788 0.266979058 −2.312805466 76 caner vs cancer 21-456224 −19.00487584 0.053035206 −0.287082772 −0.159022737 95 caner vs cancer  1-2130902 −17.85100263 −0.296114847 −0.674120091 0.200710002 96 caner vs cancer  6-415284 −17.82263372 −0.145333786 −0.004754725 0.161187342 97 caner vs cancer 6-30238 −17.74738532 0.201307581 −0.48399364 −0.419071695 8 caner vs cancer  8-486563 −24.34195503 −0.074149402 0.136565598 −0.309646468 66 caner vs cancer 21-456223 −19.41711427 −0.169615641 −0.422104365 −0.147740017 68 caner vs cancer  9-976919 −19.19924646 0.169754429 −0.149870129 0.045179717 35 caner vs cancer 18-611439 −20.84385078 0.659900298 1.179733872 −1.28247216 36 caner vs cancer  6-329093 −20.82914197 0.111947525 −0.604232399 −0.102113798 67 caner vs cancer 15-916424 −19.20472322 1.098337579 0.123021312 −0.425357804 14 caner vs cancer 21-364215 −23.147695 −0.349765433 −0.333626994 0.209847484 63 caner vs cancer  9-976918 −19.4484243 −0.019813131 −0.338493813 −0.042379462 16 caner vs cancer 21-364214 −23.01536252 −0.053301516 0.028343845 0.177763167 61 caner vs cancer 22-378130 −19.4880008 0.308365313 0.170565161 0.122550653 60 caner vs cancer 17-803588 −19.49541887 −0.292169228 −0.024314234 −0.368713245 59 caner vs cancer  5-1715385 −19.5319759 −0.249838093 −0.956471176 −0.071441678 18 caner vs cancer 19-546461 −22.97080946 −0.388273429 −0.057401189 0.836969628 56 caner vs cancer 22-378131 −19.72884156 −0.284358361 −0.110871059 −0.131062087 19 caner vs cancer 19-546460 −22.9605052 −0.566211213 0.10298505 0.659746482 9 caner vs cancer 7-10939 −24.19828156 0.007764618 −0.684892994 −0.760831017 53 caner vs cancer  6-1704942 −19.84874402 0.378542639 −0.542555509 −0.957150419 11 caner vs cancer 7-10938 −23.95579676 0.082561272 −0.418722384 −0.561011155 47 caner vs cancer  1-2115009 −20.06586747 −0.093577505 0.302518574 0.20564016 44 caner vs cancer 15-555695 −20.15008607 −0.803302241 −0.382712364 −0.30159139 43 caner vs cancer  3-452091 −20.27929394 −0.514707374 0.371259532 −0.466839863 26 caner vs cancer  5-1675324 −21.67048751 −1.05021205 −0.49235019 0.553850706 27 caner vs cancer 15-555694 −21.57393773 0.023652533 0.240962939 0.232753361 40 caner vs cancer  10-1016060 −20.46892585 0.029381721 −0.426585276 0.033829985 39 caner vs cancer  5-1675325 −20.59032645 0.480863067 0.275781467 −0.402068556 28 caner vs cancer 10-7787  −21.5431691 0.00598852 −0.199608747 −0.170189137 29 caner vs cancer 10-7786  −21.47479725 0.038463232 −0.220956901 −0.015378708 48 caner vs cancer  6-1704943 −20.04539412 0.115741682 −0.518054639 −0.856636887 98 caner vs cancer  12-1222773 −17.72217416 0.16301047 −1.858171112 −1.216496148 0 normal vs  7-1017626 −32.6798181 −1.803275893 0.175153741 2.926283828 cancer vs cancer 25 normal vs  2-382009 −21.67679298 −0.114868278 0.267337677 0.007397738 cancer vs cancer 73 normal vs 14-556474 −19.09929792 1.028802553 1.469882366 0.740193974 cancer vs cancer 72 normal vs 13-988696 −19.10886072 0.539713717 −0.623018315 −0.526201237 cancer vs cancer 71 normal vs  1-2262968 −19.11861008 −0.639678522 −0.087686268 0.512492514 cancer vs cancer 70 normal vs 8-28798 −19.14821227 −0.650234953 −0.804653337 0.150659271 cancer vs cancer 69 normal vs 16-724596 −19.17897078 −0.181424125 −0.144884684 −0.006709982 cancer vs cancer 65 normal vs 11-646423 −19.42858974 0.137556127 0.769045641 1.010936575 cancer vs cancer 64 normal vs 11-11026  −19.42859291 −0.384110053 −0.360784146 0.149990972 cancer vs cancer 62 normal vs 22-222884 −19.47573625 −0.639960957 −0.778683987 −0.556777877 cancer vs cancer 58 normal vs 10-38238  −19.67767651 0.765456023 1.021326356 0.391045996 cancer vs cancer 57 normal vs  7-546123 −19.69433464 −0.558118495 0.714728365 1.200526621 cancer vs cancer 55 normal vs  1-2115267 −19.73690799 −0.125706754 0.507930814 −0.305947675 cancer vs cancer 54 normal vs 11-11025  −19.82113917 −0.132624247 −0.173316324 0.057514538 cancer vs cancer 52 normal vs 17-554565 −19.87060257 −0.001212871 0.035726276 0.060919648 cancer vs cancer 51 normal vs 16-724595 −19.87484907 −0.698771621 −0.122429418 0.213552543 cancer vs cancer 50 normal vs  10-1188922 −19.94415652 0.526723185 0.917937922 −0.086531139 cancer vs cancer 46 normal vs  10-1188921 −20.0927365 −0.078902836 0.164631916 −0.387873458 cancer vs cancer 45 normal vs  2-2402599 −20.12830443 0.598962307 1.134520813 0.5877791 cancer vs cancer 42 normal vs  2-1139315 −20.3243112 −0.633540516 0.613485917 1.167536542 cancer vs cancer 41 normal vs 10-61622  −20.42327924 0.005372067 0.196382224 −0.085414271 cancer vs cancer 38 normal vs  1-2115266 −20.69111164 −0.769131956 0.345916524 1.254836456 cancer vs cancer 37 normal vs  3-1018088 −20.80693297 0.609398167 −0.090557439 −0.766058116 cancer vs cancer 74 normal vs 16-900927 −19.08800159 0.912529483 0.453859311 −0.179151851 cancer vs cancer 30 normal vs  13-1141899 −21.34469521 −1.031186323 −0.389204596 0.749708189 cancer vs cancer 75 normal vs 2-34738 −19.06924964 0.93625842 0.991715759 −0.688867756 cancer vs cancer 81 normal vs  2-1279336 −18.75400747 0.26785859 0.679898396 1.980063473 cancer vs cancer 21 normal vs  3-1948265 −22.40390002 −0.351581796 0.278320012 0.670590713 cancer vs cancer 2 normal vs  3-1971214 −26.9760943 −1.096565994 −1.140434035 0.235954502 cancer vs cancer 23 normal vs 2-33179 −21.87723261 −0.367084719 −0.982935256 0.385724888 cancer vs cancer 22 normal vs 17-579158 −22.35886206 −0.855883545 −0.643500898 0.371049084 cancer vs cancer 12 normal vs  1-1511292 −23.69581782 −0.040188828 0.017871212 0.57857198 cancer vs cancer 20 normal vs  7-1511060 −22.42273418 −0.344691441 −0.52467607 −0.001475964 cancer vs cancer 5 normal vs  4-840359 −25.59587088 −0.908150793 −0.023595253 −0.493610136 cancer vs cancer 17 normal vs 2-33180 −22.98082672 0.031485683 0.578377676 0.561864702 cancer vs cancer 6 normal vs  3-1971213 −25.22834725 0.159158596 −0.177874427 −0.0415477 cancer vs cancer 15 normal vs  6-1344914 −23.0638498 −0.765362602 −0.056385391 0.371844191 cancer vs cancer 7 normal vs 17-579157 −24.69417969 −1.836100826 −0.49945522 1.378041275 cancer vs cancer 32 normal vs  5-1729823 −21.10727499 0.49063763 0.442695007 0.228719149 cancer vs cancer 13 normal vs 10-61621  −23.33984193 −0.732975123 −0.170666754 0.853088439 cancer vs cancer 31 normal vs  10-1341418 −21.14588727 −0.360228601 −0.189755315 0.322305225 cancer vs cancer 49 normal vs 16-855515 −19.97033653 −0.913916851 0.004403469 0.352850662 cancer vs cancer 33 normal vs  7-353011 −21.10478683 0.560990981 1.26871861 0.300870717 cancer vs cancer 34 normal vs  2-2402598 −20.95079767 0.560267322 0.568318096 0.352815469 cancer vs cancer 91 normal vs  6-1574305 −18.45922482 0.102685453 0.425266208 −0.273613465 cancer vs cancer 87 normal vs  2-690270 −18.57780227 0.740025901 −0.409429644 −0.359030027 cancer vs cancer 86 normal vs  6-1574304 −18.58806884 −0.095348947 0.333302114 0.04190651 cancer vs cancer 84 normal vs 16-855514 −18.62861605 0.020279096 0.051846711 −0.155772388 cancer vs cancer 80 normal vs 17-765013 −18.86145879 0.791465144 0.476433898 0.192061881 cancer vs cancer 24 normal vs  2-382010 −21.732045 −0.230367057 0.218145876 −0.201298246 cancer vs cancer

Table 3 shows. Characteristics of 10 MCBs in BCC and 12 MCBs in LUNC prediction of prognosis. Markers, MCB names; Target ID, initial cg markers used to perform targeted capture sequencing; Ref Gene, genes overlap with a MCB.

Cancer type Markes Target ID Ref Gene HCC X12.939663 cg11225410 SOCS2 X13.435663 cg00338116 EPSTI1 X2.704752 cg00552226 TIA1 X4.14001 cg24496475 CH4: X6.262503 cg05414338 CH6: X6.271255 cg12041340 CH6: X6.283040 cg08343881 ZNF323 X6.415284 cg03431741 FOXP4 X6.733300 cg17126142 CH6: X8.1025045 cg18004756 GRHL2 LUNC X8.538511 cg26205771 NPBWR1 X2.2355288 cg08436738 CH2: X2.698469 cg01604601 AAK1 X6.1009129 cg27252696 SIM1 X10.1205149 cg24917945 C10orf46 X17.803588 cg11252953 C17orf101 X22.321497 cg03550506 DEPDC5 X6.283041 cg06903569 ZNF323 X6.1009115 cg12865837 SIM1 X4.463914 cg18440897 GABRA2 X4.840359 cg19255783 PLAC8 X2.967814 cg20634573 ADRA2B

Table 4 shows clinical characteristics of study cohort.

TCGA TCGA GSE HCC LUNC Normal HCC LUNC Normal Characteristic tissue tissue blood blood blood blood Total (n) 377  827  754 654  654  865  Gender Female-no.(%) 122 (32.4) 340 (41.1) 401 (53.2) 79 (12.1) 219 (33.5) 429 (49.6) Male-no.(%) 255 (67.6) 487 (58.9) 353 (46.8) 548 (83.8) 415 (63.4) 418 (48.3) NA 0 0 0 27 (4.1) 20 (3.1) 18 (2.1) Age (years) Mean 61  68  63 54  59  47 Range 16-90 33-90 19-101 15-84 28-85 19-90 Pathology Hepatocellular carcinoma 367 (97.3) 0 NA 654 (100) 0 NA Adenocarcinoma 0 458 (55.4) NA 0 338 (51.7) NA Squamous cell carcinoma 0 369 (44.6) NA 0 229 (35.0) NA Small Cell Lung Cancer 0 0 NA 0 65 (9.9) NA Others 10 (2.7) 0 NA 0 22 (3.4) NA Stage I 175 (46.4) 424 (51.3) NA 96 (14.7) 49 (7.5) NA II 87 (23.1) 115 (13.9) NA 101 (15.4) 42 (6.4) NA III 86 (22.8) 261 (31.6) NA 342 (52.3) 126 (19.3) NA IV 6 (1.6) 25 (3.0) NA 81 (12.4) 394 (60.2) NA NA 23 (6.1) 2 (0.2) NA 34 (5.2) 43 (6.6) NA Tumor burden Tumor free 236 (62.6) 503 (60.8) NA 156 (23.9) 126 (19.3) NA With tumor 114 (30.2) 159 (19.2) NA 495 (75.7) 513 (78.4) NA NA 27 (7.2) 165 (20.0) NA 3 (0.4) 15 (2.3) NA EGFR status Wide type NA 400 (48.4) NA NA 90 (13.7) NA Mutation NA 100 (12.1) NA NA 54 (8.3) NA NA NA 327 (39.5) NA NA 510 (78.0) NA Hepatic Postive 120 (31.8) NA NA 623 (95.3) NA 346 (40.0) Negtive 119 (31.6) NA NA 10 (1.5) NA 505 (58.4) NA 138 (36.6) NA NA 21 (3.2) NA 14 (1.6) Smoking Current smoker NA 725 (87.7) NA NA NA 180 (20.8) Non-smoker NA 80 (9.7) NA NA NA 671 (77.6) NA NA 22 (2.6) NA NA NA 14 (1.6)

Table 5 shows multivariate survival analysis for HCC patients and LUNC patients with composite-score of methylation markers relevant variables.

HCC LUNC Exp Se Exp Se Factor Coef (coef) (coef) z p Coef (coef) (coef) z p cp-score 0.37 1.45 0.169 2.20 0.03 0.28 1.33 0.12 2.39 0.0017 Gender 1.04 2.83 0.76 1.36 0.17 0.66 1.93 0.44 1.50 0.13 Age −0.01 0.99 0.02 −0.32 0.75 0.02 1.02 0.02 0.85 0.39 Stage 1.14 3.11 0.46 2.49 0.01 1.42 4.12 0.56 2.53 0.01 AFP 7.15e−6 1.00 5.11e−6 1.40 0.16 NA NA NA NA NA

Table 6 shows an illustrative list of 1000 markers (shown with cg identifier) described herein. In some instances, the markers are used in vs. lung analysis.

cg20424833 cg23993235 cg08128768 cg26954736 cg02019444 cg16243359 cg19003412 cg09363194 cg07366553 cg00177290 cg18442524 cg06615380 cg14063008 cg09555818 cg06105778 cg11818438 cg03388786 cg24864413 cg07168204 cg06197966 cg12177087 cg23693289 cg22700686 cg16038738 cg07676920 cg06387141 cg27433062 cg26734040 cg24787470 cg26453360 cg26944011 cg05894734 cg15045356 cg02149069 cg27183818 cg00177496 cg19340420 cg06302295 cg16264966 cg01428750 cg17516247 cg01053621 cg20385508 cg02395363 cg27499925 cg18901045 cg00599393 cg25060172 cg22203219 cg22356324 cg06605158 cg04359558 cg03607648 cg12092939 cg23352146 cg16962683 cg02702614 cg24860886 cg10505610 cg21487856 cg25734035 cg05590053 cg21103992 cg03422204 cg22809315 cg12269002 cg20701182 cg20986726 cg08822227 cg04113200 cg07155478 cg13747967 cg20163796 cg05673882 cg09225388 cg02734326 cg22862526 cg06815112 cg07354371 cg01994290 cg01144225 cg03399971 cg10110271 cg18899777 cg12477903 cg11828446 cg06429887 cg14596589 cg13755546 cg12332526 cg24803202 cg03038685 cg03228760 cg25096745 cg18440692 cg12433486 cg26703661 cg00983904 cg07360250 cg26996201 cg07571734 cg00899659 cg16492597 cg08110861 cg18185189 cg19418951 cg14313833 cg06601685 cg00498089 cg11225357 cg02280309 cg18811130 cg04739306 cg06235390 cg02927327 cg19616807 cg20822579 cg01855070 cg18281418 cg00456086 cg16107172 cg05583848 cg20011134 cg01289874 cg00026222 cg18560264 cg18234130 cg03550864 cg07823562 cg21058973 cg02633924 cg00339769 cg11083235 cg24869195 cg05597349 cg02379560 cg26313188 cg12679230 cg27650175 cg04132540 cg14221460 cg05803361 cg13788381 cg03238516 cg07054502 cg13858803 cg14480249 cg01518459 cg08597345 cg11122795 cg20365867 cg13092487 cg01145544 cg11816229 cg13197551 cg00848594 cg24461337 cg19026124 cg20926720 cg18203965 cg26536164 cg23983315 cg18668780 cg27598168 cg05020685 cg24787755 cg25962774 cg19324462 cg11743827 cg01703291 cg26173997 cg02571470 cg01224949 cg03010018 cg08521225 cg03792768 cg23646375 cg12455762 cg09437283 cg08315613 cg07000334 cg26797073 cg14463853 cg07981910 cg26318502 cg26570844 cg15882878 cg14520423 cg02852421 cg01312997 cg16689634 cg15681358 cg15903170 cg07175582 cg11962524 cg02004979 cg16326402 cg21641834 cg03971227 cg13527922 cg00211337 cg20378628 cg15425827 cg19480965 cg00363813 cg13311607 cg14031491 cg05870586 cg20852226 cg01054402 cg26386472 cg01204911 cg13039082 cg20814312 cg00150245 cg25296314 cg23241914 cg01749347 cg21303763 cg15983520 cg02831587 cg14855292 cg13324103 cg00281551 cg07819160 cg07987843 cg11857033 cg11667117 cg10075819 cg08879470 cg01566199 cg26975459 cg10201084 cg04918831 cg10097651 cg01500055 cg27075104 cg22413603 cg06400745 cg25928199 cg00586551 cg06215107 cg02796621 cg24941681 cg03661299 cg23736307 cg19786733 cg21092551 cg10881745 cg11864490 cg00590761 cg03977657 cg15264681 cg01016592 cg25302704 cg17430228 cg00803758 cg26283127 cg07560587 cg17167468 cg04919234 cg26051755 cg16508068 cg14978242 cg21663580 cg07224221 cg03743205 cg17184704 cg05283597 cg25757470 cg02787991 cg14381919 cg10550308 cg21105227 cg05772850 cg06410591 cg11154552 cg20661985 cg09866303 cg19358738 cg02488887 cg14864022 cg13860006 cg26595520 cg16782885 cg09203199 cg20038493 cg21059834 cg09004691 cg08200419 cg16814786 cg26164269 cg05299048 cg25021247 cg25686746 cg20441502 cg14804000 cg15632164 cg13583230 cg02989244 cg23400002 cg09634469 cg27079341 cg02183564 cg21380181 cg23112360 cg05143530 cg21910390 cg20024687 cg05467676 cg16619721 cg20961591 cg16674020 cg10321723 cg16608731 cg14672128 cg12797594 cg03137177 cg00788204 cg27029623 cg00571809 cg09222367 cg25934700 cg19925558 cg09147400 cg05584597 cg13081600 cg15311822 cg02179764 cg00410895 cg13502346 cg11726288 cg06098215 cg21336594 cg07359306 cg09753772 cg26259171 cg11664818 cg27094698 cg00456593 cg03710719 cg20967139 cg16414472 cg08550839 cg04213384 cg17952661 cg04520704 cg00543474 cg06625767 cg20266715 cg02954562 cg10572969 cg11233163 cg19794481 cg27083891 cg19111459 cg02622803 cg07467716 cg07054226 cg03705926 cg22501243 cg06801028 cg21195185 cg18830083 cg03781123 cg26432459 cg20227860 cg07546508 cg23065927 cg07380907 cg16129213 cg14126493 cg23953820 cg06655216 cg10581876 cg16973107 cg19898668 cg08881019 cg02363202 cg26249100 cg23404973 cg08373169 cg06573088 cg02720618 cg25573884 cg22198044 cg17198308 cg20123637 cg21487509 cg17344048 cg05718253 cg21369890 cg25479682 cg09758490 cg19324627 cg06143864 cg05142677 cg02675308 cg06250720 cg23613051 cg25781162 cg11904686 cg17001034 cg01595997 cg05159909 cg10637370 cg19580263 cg27143326 cg06828015 cg19424265 cg02580005 cg24738592 cg26530045 cg26250609 cg17927096 cg07435445 cg00949446 cg00081919 cg22747380 cg04111789 cg07216194 cg21622186 cg27436184 cg19295951 cg27264181 cg11566244 cg01959238 cg21186296 cg04355435 cg03326059 cg23982212 cg05821186 cg24239961 cg02258444 cg05876864 cg06485940 cg20205477 cg05853772 cg11254527 cg03798986 cg12544678 cg02586182 cg22056336 cg25677092 cg10494703 cg14738670 cg01979888 cg11327659 cg09747827 cg14287403 cg10543634 cg01733599 cg05095158 cg18361815 cg02288341 cg16423337 cg16919420 cg02919168 cg07839536 cg09658645 cg04221886 cg18681028 cg00264441 cg19571617 cg05845533 cg08328777 cg14052210 cg24794433 cg07681696 cg19101893 cg06498232 cg09426825 cg16560256 cg27639142 cg03123289 cg03603211 cg05967596 cg13019092 cg18229134 cg26173847 cg25391692 cg13884295 cg12296532 cg15994519 cg17792849 cg26806924 cg05450701 cg14040358 cg24412501 cg05337743 cg12074585 cg04801085 cg15485378 cg15058210 cg09868451 cg08505222 cg09036621 cg18495118 cg20358834 cg17841421 cg16809962 cg13905586 cg00237391 cg22110888 cg18263587 cg16078836 cg21798060 cg08148261 cg06671621 cg17582336 cg19975917 cg04920951 cg26585724 cg10426084 cg21361646 cg18565268 cg21306329 cg07395439 cg10704263 cg00294025 cg11970797 cg10022788 cg11493223 cg25658385 cg16983159 cg09436823 cg05891899 cg09738156 cg26080305 cg15806880 cg10064339 cg18495710 cg20315995 cg11553755 cg09218757 cg02584377 cg05314142 cg07053546 cg06703844 cg20135243 cg06230247 cg24672271 cg09050160 cg13016732 cg11856711 cg19269520 cg12137206 cg21850578 cg27619475 cg16046444 cg07818422 cg01687401 cg02153339 cg00686694 cg10910848 cg00071950 cg22972318 cg04528038 cg20588162 cg10483825 cg09038676 cg03806087 cg00589493 cg13400249 cg17297305 cg14960043 cg14582550 cg05353659 cg03794214 cg01061553 cg00920970 cg18659483 cg10418263 cg07146773 cg08498787 cg18718410 cg02243157 cg09502149 cg23315932 cg21451309 cg24782497 cg10993460 cg03355690 cg09350274 cg04450599 cg05068848 cg27308319 cg20896113 cg25264265 cg07948194 cg19777644 cg18477949 cg23511432 cg04809136 cg07541559 cg13510651 cg22076972 cg25393009 cg25746489 cg12848457 cg13972366 cg01004382 cg01614759 cg10387890 cg21861233 cg04640684 cg16865442 cg21912556 cg26192520 cg19533443 cg10394047 cg04867634 cg23973429 cg01754009 cg04674060 cg12716083 cg07991621 cg15963326 cg13263830 cg07064544 cg21101386 cg00672333 cg16738453 cg16147221 cg16311462 cg08469255 cg17468616 cg09599740 cg05684891 cg17875935 cg10745272 cg21376120 cg12353788 cg07532183 cg05382313 cg10009801 cg16362949 cg03923535 cg16367511 cg20957796 cg22094306 cg01419582 cg26661718 cg03064642 cg21766722 cg12547516 cg16430687 cg12329933 cg24390871 cg26457013 cg16895026 cg17591574 cg21561712 cg22463097 cg01250960 cg25588844 cg25865553 cg27173151 cg18482593 cg18037808 cg00394718 cg24396400 cg20482113 cg22234930 cg24220749 cg17275162 cg18309286 cg02900995 cg27297851 cg13050240 cg02579136 cg20018469 cg21574349 cg20199333 cg12022772 cg12528056 cg19243330 cg13579752 cg07076751 cg10356463 cg12699648 cg22310770 cg11990902 cg22885332 cg06525453 cg24327723 cg12209075 cg10230442 cg17643864 cg15111065 cg13077930 cg16789129 cg10344081 cg26099316 cg05852537 cg24036039 cg27573591 cg00299943 cg26301689 cg00001791 cg02607544 cg23278604 cg19465165 cg07054668 cg23090870 cg03023152 cg25023684 cg01710886 cg17330652 cg04437648 cg15657641 cg07891507 cg22416916 cg04060356 cg02523400 cg09858224 cg12870750 cg15851389 cg04590184 cg15978561 cg15783800 cg24441911 cg25488206 cg06370855 cg26686009 cg18964954 cg12624523 cg20267408 cg08804892 cg10056096 cg11364420 cg12191938 cg12149319 cg17252884 cg01819912 cg11614622 cg13341720 cg10195295 cg08310176 cg05132222 cg27321913 cg05799169 cg13153661 cg04581004 cg15480897 cg15889012 cg19086110 cg13255542 cg14430591 cg13863396 cg25247596 cg18966819 cg02227867 cg13984173 cg05721858 cg04394267 cg08508763 cg26940122 cg21107197 cg25210936 cg07285675 cg24965479 cg02300825 cg12332316 cg06285337 cg04917391 cg01519063 cg20218614 cg19484481 cg03930970 cg24247370 cg10599693 cg03464224 cg20381798 cg00061039 cg06076794 cg08988797 cg27255115 cg10604002 cg12817107 cg18782488 cg02466113 cg19875375 cg17218270 cg14164596 cg07046969 cg14350120 cg04508216 cg00709775 cg04431596 cg09349409 cg06271128 cg16408565 cg02647929 cg02573551 cg01770400 cg20134984 cg20594304 cg26426488 cg04849878 cg00971369 cg16178743 cg17266515 cg03723716 cg24354101 cg18563999 cg22684041 cg15310162 cg02923485 cg03284640 cg00487187 cg20381020 cg12083852 cg06594281 cg12420472 cg13184814 cg01347228 cg20250722 cg10826043 cg07946277 cg15834395 cg12180551 cg19115272 cg03771456 cg02672397 cg16452651 cg05696406 cg21987921 cg00948524 cg12301972 cg03755115 cg09146232 cg00547530 cg26619894 cg25033767 cg19784816 cg10674105 cg20253551 cg20976787 cg22709202 cg22032528 cg05005609 cg07182753 cg00004082 cg19024980 cg16364495 cg26870803 cg17557340 cg00630164 cg13060154 cg25669504 cg24938743 cg14343701 cg02465427 cg00371195 cg04682699 cg16310003 cg24105685 cg11641102 cg23613857 cg24789424 cg07297322 cg13707645 cg16581800 cg25862768 cg02206323 cg24432073 cg04413147 cg10500733 cg26166817 cg20817175 cg15202104 cg00161955 cg26288249 cg16908948 cg06120000 cg16959747 cg21225170 cg19291696 cg24877842 cg09130035 cg07061500 cg05447556 cg26859666 cg23625458 cg05903736 cg23661013 cg08643852 cg21019174 cg06228542 cg06431702 cg00238662 cg16578104 cg00428526 cg12985235 cg20059312 cg26418880 cg04208434 cg24677674 cg15451948 cg17770034 cg08688393 cg11115622 cg24980213 cg23295181 cg04195454 cg26520930 cg15951557 cg25252585 cg18660987 cg16348316 cg02535674 cg20823137 cg13187820 cg15679052 cg23718606 cg23715029 cg10899768 cg04257163 cg08875705 cg01012445 cg15741583 cg19155007 cg15352367 cg19300937 cg02380715 cg05654197 cg26474124 cg00104348 cg24928687 cg09243454 cg04667640 cg16924822 cg02374207 cg07686392 cg03168947 cg09502865 cg03315456 cg22647018 cg02066638 cg18094261 cg27021666 cg24163360 cg24319651 cg16943019 cg25957092 cg00647246 cg06743552 cg23743114 cg17578639 cg07968770 cg06945399 cg17655624 cg15468521 cg25371267 cg06391412 cg20367521 cg02119927 cg12912920 cg23041410 cg00953211 cg07960067 cg13214149 cg11972305 cg09070316 cg06237151 cg19554037 cg02285263 cg07871590 cg10426318 cg10085305 cg20834178 cg19606103 cg03946762 cg01991967 cg13159388 cg08173959 cg27061836 cg13175850 cg08457620 cg11274314 cg10008947 cg22392666 cg03228065 cg18024037 cg14003974 cg23011886 cg03367387 cg17142134 cg20970904 cg20836156 cg21874862 cg10857558 cg05009389 cg27278017 cg20314331 cg07294541 cg25938530 cg19347790 cg16032621

Table 7 shows ail illustrative list of 1000 markers (illustrated with cg identifier) described herein. In some cases, the markers are used in HCC-lung-blood

cg11252953 cg14247287 cg03684062 cg04517263 cg25671438 cg12835012 cg22008490 cg25922751 cg04411201 cg25686087 cg15136410 cg11601297 cg13017813 cg03636488 cg05614346 cg00206490 cg08430407 cg16332159 cg18317439 cg15626285 cg26363363 cg02874908 cg25739715 cg13828227 cg18440839 cg01742370 cg20855160 cg00015530 cg26769700 cg06788514 cg07222861 cg22882523 cg25006077 cg25408950 cg04466898 cg09366118 cg14430943 cg16460342 cg03593259 cg08546016 cg11677891 cg23229016 cg25490145 cg06482498 cg24100671 cg22582875 cg09254318 cg09399878 cg04314978 cg17518965 cg16927606 cg00466334 cg18493214 cg04234016 cg17745697 cg17742416 cg26668608 cg17653203 cg00903584 cg26585899 cg14386624 cg02756056 cg09097632 cg08098128 cg06039074 cg24088438 cg26955540 cg15573664 cg05693489 cg05596756 cg02053964 cg14642045 cg23545458 cg08055663 cg15728692 cg11214001 cg07579839 cg11334870 cg24706505 cg04051365 cg24605325 cg18637383 cg23807570 cg12504877 cg10542975 cg18113826 cg13415831 cg18781835 cg25023095 cg07097098 cg00302479 cg00907272 cg02871985 cg06385449 cg11884933 cg07410339 cg08433504 cg20873136 cg15698795 cg11803771 cg27309142 cg21122474 cg06230847 cg04848682 cg11791526 cg18610205 cg09414948 cg09130535 cg27360727 cg10393744 cg19047804 cg14191688 cg16622899 cg18982286 cg18128058 cg09383860 cg07924081 cg15570035 cg11779113 cg18384097 cg02286506 cg19400179 cg03094134 cg16289449 cg14654385 cg27540367 cg23130731 cg27198931 cg14134003 cg13165140 cg12900649 cg08387014 cg07860918 cg19675731 cg13925432 cg03807883 cg02673417 cg21171858 cg09031790 cg19382157 cg23612220 cg07380021 cg04523868 cg23013029 cg17809595 cg21830050 cg07884764 cg07420137 cg02798280 cg25666403 cg13085553 cg03750478 cg22566142 cg26315985 cg01903374 cg21376733 cg16649298 cg19459094 cg26681847 cg02556655 cg04490178 cg01447914 cg09335715 cg27388962 cg11217193 cg25468516 cg01440934 cg03254137 cg07644807 cg20563971 cg23677243 cg26899651 cg22331159 cg02481714 cg10511890 cg14519350 cg00220455 cg22742001 cg27588321 cg18571045 cg07584494 cg01765930 cg19693177 cg08715187 cg08052292 cg19619585 cg06811300 cg05959508 cg17126555 cg20136100 cg02185248 cg17984956 cg01657186 cg01314252 cg07800907 cg27405400 cg20847580 cg05304979 cg21232937 cg15128334 cg13790288 cg11685391 cg20556803 cg27125093 cg06488150 cg22935317 cg27016579 cg03964696 cg01645955 cg25953692 cg11105292 cg19300307 cg01409343 cg02367921 cg19238349 cg25210609 cg10673833 cg26680502 cg01660934 cg02844611 cg17207512 cg15309361 cg26471058 cg15095913 cg16266227 cg02522196 cg02914427 cg17697835 cg15202213 cg23128495 cg20694619 cg16061668 cg20468939 cg10351284 cg21159568 cg22216196 cg04438229 cg00045607 cg01722297 cg08632810 cg16119522 cg07915896 cg26584120 cg06908857 cg06130354 cg26112797 cg25039325 cg11753750 cg27263049 cg20720686 cg08133848 cg21293216 cg15341833 cg00933696 cg13512830 cg13612207 cg06379589 cg06901890 cg19266387 cg12737392 cg06153925 cg18281939 cg12535569 cg03109921 cg20306863 cg20781880 cg03891050 cg23093496 cg01598009 cg17013990 cg21238234 cg22550549 cg20812370 cg25954028 cg01456691 cg24686918 cg26647135 cg27366766 cg09888229 cg23742233 cg24301930 cg27262415 cg22507723 cg08144675 cg16749930 cg23281123 cg13904214 cg14083015 cg13597051 cg08574423 cg27365208 cg15059065 cg16215084 cg19923326 cg22513455 cg21604136 cg18918538 cg07041323 cg00903825 cg05369623 cg07948875 cg26769927 cg02776283 cg08781403 cg14303478 cg01603912 cg01647936 cg15759721 cg13555415 cg05798125 cg08529481 cg27182070 cg01335087 cg17936488 cg11550234 cg13395086 cg27366280 cg26696162 cg17652424 cg08120831 cg20985399 cg00032912 cg20695297 cg07589991 cg15198736 cg00058449 cg12690996 cg16802439 cg05398700 cg00055073 cg15738800 cg13695076 cg09804858 cg23547892 cg25403205 cg22552736 cg03431741 cg15139596 cg05617650 cg01921773 cg26280326 cg17705615 cg05352688 cg25432518 cg02358862 cg14088196 cg26433975 cg18973863 cg12423658 cg12360046 cg27377213 cg15902390 cg25225070 cg00282244 cg03781748 cg02565062 cg26112909 cg10590292 cg15465439 cg24742520 cg24504194 cg17750114 cg14154456 cg15704521 cg12098228 cg08224920 cg25996077 cg11143193 cg06765217 cg16274890 cg20251225 cg24166457 cg04330884 cg23254569 cg20609302 cg00024494 cg11201532 cg02041484 cg06825448 cg02512559 cg01990910 cg00447208 cg06753787 cg09972881 cg06068897 cg00558804 cg26402555 cg25338707 cg16792302 cg23764129 cg07181702 cg06787669 cg01681367 cg20784950 cg01243072 cg02331198 cg25996553 cg06449094 cg05214235 cg21004490 cg11151942 cg23663476 cg04179740 cg16044050 cg13581832 cg08330349 cg25574765 cg16782524 cg23060513 cg18183242 cg03573679 cg11953794 cg21584234 cg27141915 cg12629796 cg03654273 cg13408086 cg19005335 cg17651972 cg07939743 cg25982880 cg25765104 cg08331345 cg03234777 cg12655137 cg09169923 cg13476072 cg06380123 cg03900314 cg00188654 cg07033722 cg03308839 cg03187073 cg17974145 cg06393830 cg15172966 cg15123730 cg08352774 cg18812353 cg15878616 cg06739107 cg04514998 cg03087897 cg21208744 cg27040700 cg25739938 cg21900653 cg07701307 cg03466198 cg25946374 cg21781157 cg20432732 cg07542476 cg09731946 cg24619378 cg20046343 cg06928952 cg26691434 cg11599269 cg04273604 cg13987606 cg21282907 cg08480068 cg00037681 cg16176600 cg21133433 cg21605283 cg25499543 cg01437204 cg03549705 cg03236137 cg26488183 cg19102955 cg23554164 cg16063587 cg16863795 cg09844573 cg12970155 cg20069407 cg10045909 cg15722438 cg11051752 cg07902156 cg15046123 cg23966363 cg14584535 cg22840361 cg00287536 cg24702826 cg05997414 cg24480810 cg27025137 cg25323554 cg08947915 cg00253248 cg09153080 cg07417146 cg11436362 cg21086153 cg26306638 cg12359001 cg14145194 cg10104480 cg03128029 cg14999168 cg08097359 cg18215449 cg12005186 cg27217214 cg20389678 cg20675040 cg22589778 cg18842353 cg17910105 cg19846295 cg15921911 cg01383911 cg19663795 cg12353452 cg27219182 cg13549444 cg16659773 cg10020520 cg17920246 cg18456508 cg08912922 cg00604454 cg09684112 cg02431531 cg13693517 cg27570661 cg12242373 cg06439655 cg04936619 cg02609337 cg14858784 cg15752436 cg07380416 cg07979757 cg12967050 cg26427090 cg09638208 cg20667480 cg08905080 cg16127573 cg20107506 cg00257775 cg13832670 cg17301248 cg25913761 cg26340050 cg18854765 cg07249939 cg08075204 cg05905531 cg17009616 cg24459792 cg02189760 cg11293275 cg25598890 cg11264392 cg01647308 cg05408317 cg25451702 cg00041666 cg11612905 cg19717347 cg01797036 cg16191087 cg14539363 cg24769398 cg14677983 cg05048927 cg22662844 cg05769344 cg15797834 cg15249357 cg05387269 cg24757533 cg07052231 cg07739604 cg06064964 cg26017930 cg12617080 cg03879823 cg17080697 cg22694271 cg00501765 cg15554421 cg01760983 cg13853198 cg15724534 cg03409108 cg20030711 cg07905808 cg13924996 cg21249754 cg16302816 cg23506842 cg15240033 cg10510259 cg16824282 cg13639901 cg05878887 cg05751148 cg04515806 cg13827209 cg17395211 cg15844596 cg10530767 cg03263730 cg08253808 cg24491784 cg21757281 cg10640333 cg22143387 cg24107852 cg15334372 cg05656900 cg21792134 cg25116200 cg17583432 cg16324409 cg07641284 cg11176159 cg20971407 cg14311559 cg09006487 cg01838544 cg17911539 cg18901104 cg14345572 cg01787084 cg01016122 cg03419014 cg18071865 cg07732037 cg19704755 cg24420089 cg06119477 cg00750430 cg09152089 cg25363807 cg02597698 cg26683005 cg25384897 cg08454507 cg00442389 cg13931640 cg11744304 cg12079904 cg09866569 cg18887230 cg26038697 cg08400494 cg20366239 cg00164997 cg08859309 cg26708220 cg03187301 cg20071624 cg21524899 cg10836110 cg05344747 cg06311355 cg11600161 cg23824762 cg06620723 cg03606269 cg25774643 cg25143508 cg03396151 cg09762242 cg06340367 cg02385661 cg06405563 cg01008602 cg08176258 cg10883375 cg00816037 cg08313420 cg03569637 cg17875657 cg26094805 cg17006136 cg06087349 cg04859102 cg21455600 cg15035143 cg23128949 cg17319795 cg11796996 cg19563932 cg13912307 cg07499032 cg06173520 cg12379383 cg11080651 cg03126713 cg25440680 cg07832674 cg22190721 cg02127509 cg15625671 cg24484352 cg19797087 cg17178291 cg00759427 cg00118342 cg01382502 cg05653018 cg01923089 cg00747922 cg02646515 cg24247537 cg15046675 cg09190408 cg04655481 cg15011899 cg01554529 cg15035133 cg01620360 cg05931497 cg12401842 cg06912814 cg17346145 cg09572125 cg04567323 cg08858130 cg16924010 cg02744216 cg19321684 cg27226424 cg10611016 cg12686260 cg00899907 cg26118759 cg08486903 cg07786220 cg02450981 cg25251478 cg26149678 cg01604601 cg17707057 cg24835948 cg07946977 cg24202468 cg20887442 cg18457597 cg18856478 cg02233149 cg17067993 cg05711036 cg12281620 cg19747195 cg15305732 cg23589035 cg11860434 cg21108554 cg04493247 cg16107907 cg17344770 cg10186131 cg08351710 cg23021796 cg03032552 cg02042997 cg13399952 cg21426759 cg07137244 cg20336172 cg26394055 cg04531756 cg10732611 cg13616508 cg11826104 cg07428959 cg23147227 cg20685713 cg08670658 cg15027815 cg12116137 cg18696027 cg00360761 cg20826709 cg19421584 cg04990190 cg14741474 cg11094248 cg16268734 cg07116712 cg07119472 cg20610594 cg21461981 cg22258045 cg22463795 cg20300794 cg00025044 cg07573366 cg09001226 cg18002447 cg08485187 cg12192282 cg05167561 cg27023597 cg00242035 cg03603669 cg20783697 cg13561409 cg13383814 cg22980156 cg03718241 cg13911392 cg04147906 cg00407537 cg00228142 cg18960620 cg14714629 cg19192256 cg16402452 cg10491452 cg01209642 cg05287480 cg27063969 cg06576340 cg10402417 cg17122157 cg07549381 cg23797100 cg03339537 cg26808687 cg01174743 cg12269161 cg14556909 cg23950278 cg16398761 cg03956042 cg01284289 cg16389078 cg03228145 cg00916899 cg03979258 cg00997280 cg14516183 cg23304647 cg25484252 cg20411756 cg21311067 cg08161480 cg15159247 cg20485084 cg04192740 cg17309904 cg05696678 cg00862408 cg20425130 cg15678817 cg01925950 cg03758697 cg22894329 cg04784315 cg03218479 cg08132573 cg09354037 cg18966401 cg27121758 cg13468144 cg14998613 cg26836479 cg18081760 cg26133301 cg20066792 cg14282004 cg19645616 cg00878163 cg07682037 cg03762237 cg07921503 cg10456459 cg16986578 cg16590794 cg16350446 cg22460896 cg01816936 cg04858553 cg05667818 cg27307183 cg24691453 cg12067423 cg19982684 cg05979232 cg22215508 cg23549534 cg26514623 cg03297901 cg13493526 cg03331514 cg23429794 cg13487445 cg03699843 cg25662041 cg02657012 cg12042264 cg23878564 cg18942579 cg20165946 cg02145701 cg14343513 cg00048436 cg08241318 cg16509569 cg26572452 cg12033822 cg27617225 cg21452808 cg23595413 cg19021985 cg12086464 cg16260696 cg21164509 cg04726013 cg08928675 cg14315912 cg24690709 cg09419005 cg19721787 cg05177060 cg12678686 cg06508867 cg24126180 cg03089651 cg02782634 cg14564351 cg05399244 cg16958939 cg01498832 cg24032691 cg24525952 cg12192582 cg09331545 cg20510285 cg08796391 cg00327355 cg19433118 cg13857210 cg01951274 cg19193595 cg18184218 cg20181887 cg03773413 cg12436568 cg02116768 cg16911583 cg10783294 cg11410920 cg18581405 cg01765152 cg15292446 cg24948743 cg13821008 cg13464240 cg15520845 cg03649649 cg26842815 cg05938958 cg01448132 cg15569052 cg05098343 cg08357436 cg11869499 cg04992150 cg24545967 cg03275851 cg10055231 cg03190513 cg20308351 cg10001715 cg03190661 cg04690998 cg00388871 cg04441857 cg12123728 cg26077811 cg02037425 cg23184252 cg23352695 cg14788563 cg19055828 cg24234270 cg24397241 cg24668061 cg20641026 cg07938743 cg04552555 cg17285931 cg22801913 cg11888747 cg03106245 cg04943741 cg17359629

Example 2

Primary Tissue Patient Data

Both primary solid tissues from cancer patients and blood tissues from healthy donor were measured by Illumina 450k infimum bead chip. Primary tumor DNA methylation data of 485.000 sites was obtained from The Cancer Genome Atlas (TCGA). Complete clinical, molecular, and histopathological datasets are available at the TCGA website. Individual institutions that contributed samples coordinated the consent process and obtained informed written consent from each patient in accordance to their respective institutional review boards. Blood tissue DNA methylation data from healthy donor were obtained and generated based on study from Hannum et al., 2013, Mol Cell 49, 359-367 (GSE40279) in which DNA methylation profiles for HCC and blood were analyzed.

Serum Sample Patient Data

A second independent Chinese cohort consisted of LUNC and HCC patients at the Sun Yat-sen University Cancer Center in Guangzhou, Xijing Hospital in Xi'an, and the West China Hospital m Chengdu, China. Patients who presented with LUNC and HCC from stage I-IV were selected and enrolled in this study. Patient characteristics and tumor features are summarized in Table 11. The TNM staging classification for LUNC and HCC is according to the 7^(th) edition of the AJCC cancer staging manual. This project was approved by the IRBs of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. Informed consent was obtained from all patients. Two prospective trials on early detection of LUNC and HCC patients using methylation markers for predicting cancer occurrence in high-risk populations were conducted. In the first study, patients were recruited from a group of smokers that were undergoing CT scan-based lung cancer screening from December 2015 to December 2016. Patients presenting with lung nodules (<10 mm, n=232, Table 14) were, selected to undergo methylation profiling at the time of screening and were subsequently followed through secondary testing to determine whether nodules were due to cancer or inflammatory or infectious conditions by tissue biopsy and pathology diagnosis verification. In the second trial, high risk patients with liver cirrhosis were enrolled (n=242).

Tumor and normal tissues were obtained as clinically indicated for patient care and were retained for this study. Human blood samples were collected by venipuncture, and plasma samples were obtained by taking the supernatant after centrifugation and stored at −80° C. before ctDNA extraction.

The pre-treatment scrum samples were obtained at the initial diagnosis, and the post-treatment serum samples were evaluated approximately 2 months after treatment, where the treatment referred to either chemotherapy or surgical resection of tumor. The primary endpoint (including response to treatment: progressive disease (PD), partial response (PR) and stable disease (SD)) was defined according to the RECIST guideline. For patients treated with surgical removal and no recurrence at time of evaluation, we assumed they had complete response (CR).

Extraction of cfDNA from Plasma

It was determined that the minimal volume of plasma required to get consistent amounts of cfDNA for targeted sequencing. As a rough guide, it was aimed at ˜20× coverage at 90% of markers covered by the padlock probe panel (sec below). It was observed that 20,000 or more total unique reads per sample fulfilled this criterion. It was found that 1.5 ml or more plasma could reliably yield enough ctDNA to produce >20,000 unique reads. The relationship between amount of cfDNA in 1.5 ml plasma and detected copy numbers was further investigated using digital droplet PCR. It was found that 1.5 ml of plasma yielded >10 ng, what produced at least 140 copies of detected amplicons in each digital droplet PCR assay. It was therefore settled on using 15 ng/1.5 ml as a cutoff in all of our experiments to obtain consistent and reliable measurements of DNA methylation.

cfDNA from 1.5 ml of plasma was extracted using EliteHealth cfDNA extraction Kit (EliteHealth, Guangzhou Youze, China) according to manufacturer's recommendations.

Bisulfite Conversion of Genomic or cfDNA

10 ng of DNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer's protocol. Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfite conversion was >99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CH (non-CG) dinucleotides.

Marker Selection for Padlock Probe Panel Design

To identify markers to differentiate HCC, LUNC and normal blood methylation signatures the “moderated t-statistics shrinking” approach cm 450k methylation data with Benjamini-Hochberg procedure was employed to control FDR at a significance level of 0.05 using pairwise comparisons of 377 HCC samples and 827 LUNC samples (TCGA) and 754 normal blood samples (GSE40279, our previous study (HANNUM REF)). The lists was ranked by adjusted p-value and selected the top 1000 markers for designing padlock probes for differentiating cancer (both LUNC and HCC) versus normal samples and a separate group of 1000 markers for differentiating LUNC versus HCC (FIG. 20).

All 2000 markers were used to design padlock probes for capture and sequencing of cfDNA. Padlock-capture of bis-DNA was based on the technique on methods of Deng, et al, 2009, Nature Biotechnology 27, 353-360; Diep, et al, 2012, Nature Methods 9, 270-272; and Porreca, et al., 2007. Nature Methods 4, 931-936; and with further modifications. Because of a relatively modest total size of captured regions/cg markers, this approach offers much lower cost of sequencing than any current methods including whole methylome-wide sequencing, therefore enabling us to evaluate a large number of samples. Furthermore, the direct targeted sequencing approach offers digital readout, and requires much less starting cfDNA material (10-15 ng) than more traditional recent methods based on hybridization on a chip (eg. Infinium, Illumina) or target-enrichment by hybridization (eg. SureSelect, Agilent). This approach is also less sensitive co unequal amplification as it utilizes molecular identifiers (UMIs).

Padlock Probe Design, Synthesis and Validation

All probes were designed using the ppDesigner software. The average length of the captured region was 70 bp, with the CpG marker located in the central 80% of the captured region. A 6 bp 6-bp unique molecular identifier (UMI) flanked capture arms to aid in eliminating amplification bias in determination of DNA methylation frequencies. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produce probes of equal length. Probes were synthesized as separate oligonucleotides (IDT). For capture experiments, probes were mixed in equimolar quantities and purified on Qiagen columns.

Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60-65% of captured regions with coverage >0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at >0.5× of average coverage.

Targeted Methylation Sequencing Using Bis-DNA Padlock Probe Capture

10 ng of bisulfite-converted DNA was mixed with padlock probes in 20 μl reactions containing 1× Ampligase buffer (Epicentre). To anneal probes to DNA, 30 second denaturation at 95% was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second and incubation for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2 U of PfuTurboCx polymerase (Agilent), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in 1× Ampligase buffer. After 5-hour incubation at 55° C., reactions were denatured for 2 minutes at 94° C., 5 μl of exonuclease mix (20 U of Exo I and 100 U ExoIII of, Epicentre) was added and single-stranded DNA degradation was carried out at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C.

Circular capture products were amplified by PCR using printers specific to linker DNA within padlock probes. Both primers contained 10 bp barcodes for unique dual-index multiplexing, and Illumina next-generation sequencing adaptor sequences. PCR was performed as follows: 1× Phusion Flash Master Mix, 3 μl of captured DNA and 200 nM primers, using the following cycle: 10 s at 98° C., 8× of (1 s at 98° C., 5 s at 58° C., 10 s at 72° C.), 25× of (1 s at 98° C., 15 s at 72° C.), 60 s at 72° C. PCR reactions were mixed and the resulting library was size selected on 2.5% agarose gels to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp). Purity of the libraries was verified by TapeStation (Agilent) and PCR using Illumina flowcell adaptor primers (p5 and p7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries were sequenced on MiSeq and HiSeq2500 systems (Illumina) using PE 100 reads. Median total reads for each sample was 500,000 and on-target mappability 25% (˜125,000 on-target non-unique reads).

Optimization of Capture Coverage Uniformity

Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes <60-65% of captured regions with coverage >0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at >0.5× of average coverage.

Sequencing Data Analysis

Mapping of sequencing reads was done using the software tool bisReadMapper with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script. Reads were on-the-fly converted as if all C were non-methylated and mapped to in-silico conceited DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2. Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single, unique read. Methylation frequencies were calculated for all CpG dinucleotides contained within the regions captured by padlock probes by dividing the numbers of unique reads carrying a C at the interrogated position by the total number of reads covering the interrogated position.

DNA Isolation and Digital Quantitative PCR

Tumor and corresponding plasma samples were obtained front patients undergoing surgical tumor resection; samples were frozen and preserved in at −80° C. until use. Isolation of DNA and RNA from samples was performed using DNA/RNA Mini Prep kit and a cfDNA extraction kit, respectively (EliteHealth, Guangzhou Youze, China). To estimate tumor cfDNA fractions, we performed mixing experiments with various fractions of normal ctDNA and HCC tumor genomic DNA (gDNA) and assayed methylation values and copy numbers by dPCR (sec next section for details). Digital droplet PCR (ddPCR) was performed according to the manufacturer's specifications (Bio-Rad, Hercules, Calif.). The following ddPCR assay was used in this study: cg10590292-forward primer 5-TGTTAGTTTTTATGGAAGTTT, reverse primer 5′-AAACIAACAAAATACTCAAA; fluorescent probe for methylated allele detection 576-FAM/TGGGAGAGCGGGAGAT/BHQ1/-3′; probe for unmethylated allele defection, 5′/HEX/TTTGGGAGAGTGGGAGATTT/BHQ1/-3′. ddPCR was performed according to the manufacturer's specifications (Bio-Rad, Hercules, Calif.), using the following cycling conditions: 1× of 10 mins at 98° C., 40× of (30 s at 98° C., 60 s at 53° C.), 1× of 10 mins at 98° C.

Calculation of Tumor cfDNA Fraction

It was assumed that a particular methylation value observed for an HCC cfDNA sample results from the combined contribution of normal and tumor cfDNA. The fraction of cfDNA originating from the tumor was estimated using the following formula: fraction contributed from tumor DNA in sample i=[methylation value in HCC cfDNA in sample i−mean methylation value of normal cfDNA]/[(mean methylation value of tumor DNA−mean methylation value of normal cfDNA]. Using this approach, it was estimated that on average the tumor fraction is around 23% in HCC cfDNA samples. Samples were then grouped according to factors that evaluate tumor load, such as an advanced stage and pre-treatment status, since these factors are expected to affect the tumor fraction in ctDNA. Indeed, it was observed that conditions associated with a higher tumor staging and severity also tended to have a larger tumor fraction. To further vet this approach, a mixing experiment with different fractions of normal cfDNA (0-100%) and tumor genomic DNA (0-100%) was performed and assayed methylation values using digital PCR. It was shown that incremental addition of tumor genomic DNA can increase methylation fraction percentage up to the values observed in the HCC patient samples. Specifically, addition of 10%, 20%, 40%, 60% or 100% fraction of tumor genomic DNA can be predicted by the above formula, when using methylation values obtained from the experiment.

Statistical Analysts

DNA Methylation Marker Selection for Diagnostic and Prognostic Analysts

Out of 2000 initially designed padlock probes, only 1673 were informative, i.e able to give positive and specific PCR amplification signals, and thus were used as capture probes in the subsequent experiments in cfDNA samples. Sequencing depth was used as a sample inclusion criterion. Samples where less than 100 MCB (sec below) showed 10× read coverage were excluded from further analysis. Since each MCB incorporated on average ˜3 CG markers, the 10× coverage ensured at least 30 methylation measurements per MCB. Using these criteria, 73% of all samples with a median of 34K mapped reads per sample were included.

After having obtained DNA methylation data for 1673 CG markers, the concept of MCBs to merge proximal CpG markers into a MCB was used, resulting in a total of 888 MCBs. For each MCB, the MCB-specific methylation value was quantified with two numbers: log 10 (total methylated read count+1) and log 10 (total unmethylated read count+1), using the log transform to reduce outlier effects.

About 1673 informative padlock probes were obtained that were able to give positive and specific PCR amplification signals and they were used as capture probes in the subsequent experiments in cfDNA samples. cfDNA samples with less than 100 MCB of >30× coverage were also eliminated. Methylated reads for each marker were defined as total unique methylated reads and methylation values for each marker were defined as the proportion of read counts with methylation divided by total read counts.

cfDNA-Based Diagnostic Classifier Construction Using MCBs (Cd-Score)

ctDNA sample data obtained from patients diagnosed with liver cancer (HCC), lung cancer (LUNC) and normal controls were divided into training and validation cohorts. The full dataset was randomly split with a 1:1 ratio to form the training and validation cohorts.

Marker selections: Within the training cohort, the “randomized lasso” scheme was adopted to reduce the sampling dependency and stabilize variable selection in order to select biomarkers with high confidence. The training set was first randomly divided with 1:1 ratio. The variable selection procedure on two thirds of the samples was conducted and withheld a third of the samples for evaluating performance of the feature selection process. The feature selection process consisted of two steps repeated 50 times. MCBs were included for training the final model if they were selected in 40 out of 50 feature selection iteration. A multi-class prediction system based on Friedman et al, 2010, J Stat Softw 33, 1-22 was constructed to predict the group membership of samples in the test data using the panel of MCBs selected. A confusion matrix and ROC curves were also provided to evaluate sensitivity and specificity, in addition to prediction accuracy based on the held out partition of the training set.

Classification process: a two-step classification process was employed: cancer vs normal, LUNC vs HCC by building two binary multinomial logistic regression models. The multinomial logistic regression has the advantage where it can yield an intuitive probability score and allow for easier interpretation. For example, it the cancer-vs-normal model yield a probability score of 70% for a given methylation profile, it suggests that the patient has a 70% chance of having cancer. In order to minimize the number of false cancer predictions, we set the cancer prediction confidence threshold to 80%. For patients with at least 80% chance of cancer, we apply the cancer-vs-cancer regression model for classifying between LUNC and HCC, the classification model will decide only if the classified sample has a confidence of over 55%.

Building a Predictive Model for Prognosis and Survival

The potential to use a combined prognosis score (cp-score) system based on both methylation reads and non-methylated reads was investigated for each MCB in cfDNA for prediction of prognosis in LUNC and HCC in combination with clinical and demographic characteristics including age, gender, and AJCC stage. For each type of cancer, a cp score model was build and validate it by randomly selecting half of the observations front the full dataset as the training cohort, and treated the rest as the validation cohort. Variable selection on the training cohort was conducted and built the composite score on the validation cohort. Within the training cohort, the “randomized lasso” scheme was adopted to reduce the sampling dependency to stabilize the variable selection in order to select biomarkers with a high confidence. The entire cohort was randomly divided with a 1:1 ratio. The variable selection procedure was conducted on two-thirds of the training cohort. LASSO was implemented with an optimal tuning parameter determined by either the expected generalization error from the 10-fold cross validation or the information based criteria AIC/BIC, whichever yielded the highest (the proportion of explained randomness) with the selected biomarkers. The 10 most recurring features from HCC and in LUNC (Table 10) was then aggregated. To evaluate the predictability of each panel externally, a composite score was obtained for each patient in the validation cohort by multiplying the unbiased coefficient estimates from the Cox regression and the methylation reads. A Kaplan-Meier curve and log-rank test were generated using the dichotomized composite score, which formed a high-risk and low-risk group membership assignment according to its median. This segmentation was compatible with that formed by AJCC stage. Time-dependent ROC was used to summarize the discrimination potential of the composite score, AJCC stage and the combination of two, with ROC curves varying as a function of time and accommodating censored data. Finally, we also fitted a multivariate Cox regression model to assess the significance of potential risk factors.

All the analysis was conducted in R (version 3.2.3) and python (version 2.7.13) with the following packages used: ‘glmnet’, ‘limma’, ‘survival’, ‘sklearn’, ‘lifeline’, ‘survival ROC’, ‘survcomp’.

All hypothesis testing was done by two-sided with p-value <0.05 considered to be statistically significant unless specifically stated otherwise.

Patient and Sample Characteristics

Clinical characteristics and molecular DNA methylation profiles were collected for 827 LUNC and 377 HCC tumor samples from The Cancer Genome Atlas (TCGA) and 754 normal samples from a dataset used in our previous methylation study on aging (GSE40279) (Hannum et al., 2013). Two cohorts of patients were studied. The first cohort was from solid tumor samples from TCGA and the second cohort was from plasma samples from China. To study cfDNA in LUNC and HCC, plasma samples were obtained from 2,396 Chinese patients with HCC or LUNC, and from randomly selected, population-matched healthy controls undergoing routine health care maintenance, resulting in a cohort of 892 LUNC and 1504 HCC patients and 2247 normal healthy controls, informed written consent was obtained from each study participant. Clinical characteristics of ail patients and controls are listed in Table 11.

Identification of Methylation Markers Differentiating LUNC Anti HCC and Blood

Previous reports indicate that plasma contains DNA released from tissues within the body. It was hypothesized that because cfDNA originating from tumor cells can be detected in a background of cfDNA predominantly released from leukocytes, CpG markers with a maximal difference in methylation values between LUNC or HCC versus normal leukocytes would be most likely to demonstrate detectable methylation differences in the cfDNA of HCC or LUNC patients when compared to that of normal controls. To identify putative markers, methylation data derived from cancer tissue DNA from the TCGA and normal blood including 827 LUNC, 377 HCC, and 754 blood samples from healthy controls were compared. In order to identify DNA sites with significantly different rates of methylation between LUNC or HCC and normal blood, a t-statistic with Empirical Bayes was used for shrinking the variance and selected the top 1000 significant markers, using the Benjamini-Hochberg procedure to control the FDR at a significance level of 0.05. Unsupervised hierarchical clustering of these top 1000 markers was able to distinguish between LUNC, HCC, and normal blood, and between LUNC and HCC (FIG. 20). About 2,000 molecular inversion (padlock) probes corresponding to these 2000 markers for capture-sequencing cfDNA from plasma (1000 for cancer versus normal and 1000 for LUNC versus HCC) were then designed.

cfDNA Diagnostic Prediction Model for LUNC and HCC

The methylation data of the 888 selected Methylation Correlated Blocks (MCB) that showed good methylation ranges in cfDNA samples were further analyzed to identify MCBs that showed significantly different methylation between cancer samples (LUNC and HCC) versus normal control samples. Unsupervised hierarchical clustering of these selected MCBs using methylated reads across samples is shown in FIG. 20C, and distributions of MCB methylated read values for normal, LUNC and HCC samples is shown in FIG. 21. The entire methylation dataset of 888 MCBs was therefore analyzed by Least Absolute Shrinkage anil Selection Operator (LASSO) method and further reduced the number of MCBs. LASSO-based feature selection identified 28 MCBs for discriminating LUNC versus HCC and normal, 27 MCBs for discriminating of HCC versus LUNC and normal, 22 MCBs for discriminating of normal vs HCC and LUNC, resulting in 77 unique markers (5 MCBs overlap between models). This approach combined the information captured by the MCBs into a composite cfDNA-based score (composite diagnostic score: cd-score). The utility of this score was evaluated for predicting the presence of LUNC or HCC using a hold-out strategy where samples were randomly assigned to a training set and a validation set with a 1:1 ratio. The scoring system was trained using 229 LUNC, 444 HCC and 1123 normal control cfDNA samples and then validated on 300 LUNC, 445 HCC and 1124 normal samples. Applying the fitted model to the validation set samples yielded a sensitivity of 92.4% for HCC and 85.8% for LUNC, and a specificity of 99.0% for normal controls in a multi-classification scheme (Table 8A). It was found that this mode could successfully differentiate LUNC and HCC samples from normal controls in the validation cohort (AUC cancer vs normal=0.979; AUC LUNC vs HCC=0.924; FIG. 14A, Table 8B, Table 8C). Unsupervised hierarchical clustering of the 77 MCBs was able to distinguish HCC and LUNC from normal controls with high specificity and sensitivity (FIG. 14C and FIG. 14D).

Liver diseases, such acirrhosis, and fatty liver, are major risk factors for HCC. Thus, the cd-score of the model was assessed for differentiating between liver diseases and. It was found that the cd-score was able to differentiate HCC patients from those with liver diseases or healthy controls (FIG. 15A). These results were consistent and comparable with those predicted by AFP levels in HCC (FIG. 15B). The cd-score could also differentiate between LUNC patients and non-LUNC patients with a smoking history (>1 pack/day for ten years) who were at an increased risk of LUNC (FIG. 15C). These results were consistent and comparable with those predicted by AFP levels in HCC (FIG. 15D).

Methylation Profiles Predicted Tumor Burden, Treatment Response and Staging

Next, the utility of the cd-score was studied in assessing treatment response, the presence of residual tumor following treatment, and staging of LUNC and HCC. In LUNC, the cd-scores of patients with detectable residual tumor following treatment (n=559) were significantly higher than those with no detectable tumor (n=160) (p<0.001, FIG. 16A). Similarly, there was good correlation between the cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p=<0.005, FIG. 16B). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=158) compared with those before surgery (n=67), yet became higher in patients with recurrence (n=56) (p<0.01, FIG. 16C). Furthermore, the cd-scores were significantly higher in patients before treatment (n=67) or with progression (n=136) compared to those with a positive treatment response (n=328) (p<0.001, FIG. 16D>. In HCC, The cd-scores of patients with detectable residual tumor following treatment (n=889) were significantly higher than those with no detectable tumor (n=314) (p<0.0001, FIG. 16K). Similarly, there was a highly positive correlation between cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p<0.001, FIG. 16F). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=293) compared with those before intervention (n=109), yet became higher in patients with recurrence (n=155) (p<0.01, FIG. 16G). Furthermore, the cd-scores were significantly higher in patients before treatment (n=0.109) or with progression (n=381) compared to those with treatment response (n=249) (p<0.001, FIG. 16H). Serial longitudinal dynamic changes were obtained of methylation values of CpG site cg10673833 in several individuals with LUNC or HCC patient in order to monitor treatment response and found there was a high correlation between methylation values and treatment outcomes (FIG. 23, FIG. 24 and FIG. 25). Collectively, the results showed the significant correlation between be cd-score (i.e., the amount of ctDNA in plasma) and tumor burden, demonstrating its utility for the prediction of tumor response and for surveillance to detect recurrence.

Diagnostic Utility of cfDNA as Compared with AFP and CEA

Despite enormous efforts, an effective non invasive blood-based biomarker for surveillance and diagnosis of LUNC and HCC is still lacking. CEA (cancer embryonic antigen) and AFP have filled this role for lung cancer and HCC for decades, but its sensitivity and specificity are inadequate. Moreover, some patterns with squamous cell carcinoma or small cell lung cancer will not have increased blood CEA levels. AFP has low sensitivity of 60%, making it inadequate for detection of all patients that will develop HCC and thus severely limiting its clinical utility. In fact, if is common for cirrhotic patients with HCC to show no increase in AFP levels. Strikingly, 30% patients of the HCC study cohort have a normal AFP value (<25 ng/ml). In biopsy-proven LUNC patients of the entire cohort, the cd-score demonstrated superior sensitivity and specificity to CEA for LUNC diagnosis (AUC 0.977 (cd-score) vs 0.856 (CEA), FIG. 16Q). Both cd-score and CEA values were highly correlated with tumor stage (FIG. 16J, FIG. 16B). On other hand, the cd-score demonstrated superior sensitivity and specificity to AFP for HCC diagnosis (AUC 0.993 vs 0.835, FIG. 4R) in biopsy-proven HCC patients. Both cd-score and AFP values were highly correlated with tumor stage (FIG. 16F and FIG. 16N). In patients with treatment response, tumor recurrence, or progression, the cd-score showed more changes from initial diagnosis than that of AFP (FIG. 16G and FIG. 16H, FIG. 16O and FIG. 16P). In LUNC patients with treatment response, tumor recurrence, or progression, the cd-score showed more significant changes from initial diagnosis than that of CEA (FIG. 16C and FIG. 16D, FIG. 16K and FIG. 16L). In LUNC and HCC patients with serial samples, there was a concomitant and significant decrease in cd-score in patients with a positive treatment response than in patients prior to treatment. There was an even further reduction in cd-score in patients after surgery. In contrast, there was an increase in methylation rate in patients with progressive or recurrent disease (FIG. 23). By comparison. CEA and AFP were less sensitive for assessing treatment efficacy in individual patients (FIG. 24 and FIG. 25).

cfDNA Prognostic Model for HCC and LUNC

The potential of using a combined prognosis score (cp-score) based on cfDNA methylation analysis for prediction of prognosis in LUNC and HCC in combination with clinical and demographic characteristics including age, gender, AJCC stage, and AFP value was investigated. Totally 599 LUNC patients and 867 HCC patients enrolled in prognosis analysis (patients without tumor burden are excluded from the analysis). The median follow up time was 9.5 months (rang 0.6-26 months) in LUNC cohort and 6.7 months (rang 1.2-21.0 months) in HCC cohort. In the HCC cohort, the training dataset contained 433 observations with 41 events and the validation dataset contained 434 observations with 58 events. By using statistical learning methods, a predictive model was constructed using 10 CpG MCBs (Table 10) that can separate the HCC cohort into high and low risk groups, with median survival significantly greater in the low-risk group than in the high-risk group (log-rank test=24.323, df=1, p<0.001) (FIG. 17A). In the LUNC cohort, the training dataset contained 299 observations with 61 events and the validation dataset contained 434 observations with 58 events. A panel of 10 CpG markers (Table 10) was able to divide the LUNC cohort into high and low risk groups, with median survival significantly greater in the low-risk group than in the high-risk group (log-rank test=6.697, df=1, p<0.001) (FIG. 17B).

Multivariate Cox regression model showed that the cp-score was significantly correlated with incidence of mortality m both HCC and LUNC. The cp-score was an independent risk factor of survival both in HCC and borderline in LUNC validation cohorts (hazard ratio=2.4881, p=0.000721 in HCC; hazard ration 1.74, p=0.068 in LUNC; p=0.0017 in LUNC; Table 12). Interestingly, when cp-score and other clinical characteristics woe taken into account in HCC, AFP was no longer significant as a risk factor (Table 13). As expected, TNM stage (as defined by AJCC guidelines) predicted the prognosis of patients both in HCC (FIG. 17C) and LUNC (FIG. 17D). The combination of cp-score and TNM staging improved our ability to predict prognosis in both HCC (AUC 0.867, FIG. 17E) and LUNC cohort (AUC 0.825. FIG. 17F).

Methylation Markers in Early Diagnosis of LUNC and HCC

Since LUNC and HCC are very aggressive cancers with poor prognosis and survival, and surgical removal of cancer at stage 1 carries a much more favorable prognosis, early detection becomes a key strategy in reducing morbidity and mortality. The method of using methylation markers for predicting cancer occurrence in high-risk populations was investigated in two prospective studies. In the first study, consecutive patients were recruited from a group of patients with solid lung nodules >10 mm identified on chest CT scans. These patients were enrolled in a study for early lung detection in smokers and underwent CT scan-based lung cancer screening. Patients presenting with a solid long nodule (between 10 mm and 30 mm in size, n=208, Table 13) were selected to undergo methylation profiling at the time of screening. These patients were subsequently followed through secondary testing to determine whether nodules were due to LUNC or a benign condition due to inflammation or infection by tissue biopsy and pathology verification. The methylation profile was sufficient to differentiate patients with biopsy-proven stage 1 LUNC lesions compared to patients with benign nodules due to inflammatory or infectious conditions (FIG. 18, Table 14). Among the patients with at least 59% confidence for diagnosis. Positive predictive value (PPV) of stage I cancer was 95.9% and negative predictive value (NPV) was 97.4%. Similarly, we prospectively enrolled high risk HCC patients with liver cirrhosis (n=236, Table 13). The methylation profile was able to predict progression to stage 1 HCC with 89.5% sensitivity and 98.2% specificity (FIG. 19, Table 14) among the patients with at least 58% confidence for diagnosis. PPV was 80.9% and NPV was 99.1%.

In this study, differentially methylated CpG sites ware first determined in LUNC and HCC tumor samples versus normal blood. Then, these markers were interrogated in the cfDNA of a large cohort of LUNC and HCC patients as well normal controls. A diagnostic model (cd-score) was developed using methylation of cfDNA to predict the presence of cancer, while at the same time differentiating between LUNC and HCC.

The cd-score discriminated patients with HCC front individuals with HBV/HCV infection, cirrhosis, anti fatty liver disease as well as healthy controls. In some instances, it is important that a serum test reliably distinguish these disease states from HCC. According to the results, the sensitivity of the cd-score for HCC is comparable to liver ultrasound, the current standard for HCC screening. In addition, in some instances it is superior to AFP, the only clinically used biomarker for HCC, making cd-score a more cost-effective and less resource-intensive approach. Furthermore, by showing its high correlation with HCC tumor burden, treatment response, and stage, the cd-score of the model demonstrated superior performance than AFP in the instant cohort (AFP values were within a normal range tin 40% of our HCC patients during the entire course of their disease). In some cases, the cd-score may be particularly useful for assessment of treatment response and surveillance for recurrence in HCC. Since nearly all of the HCC patients had hepatitis (most likely hepatitis R) in the study, HCC arising from other etiologies may have different cfDNA methylation patterns. Similar to HCC, screening for lung cancer hits a high cost, involving CT imaging of the chest, which has an associated radiation exposure and a high false-positive rate. In some cases, the cd-score reliably distinguished smokers and patients with lung cancer and may also have utility in improving screening and surveillance.

Prognostic prediction models were also constructed for HCC and LUNC from the cp-score. The cp-score effectively distinguished HCC and LUNC patients with different prognosis and was validated as an independent prognostic risk factor in a multi-variable analysis in our cohorts. Of note, for predicting prognosis in HCC, cfDNA analysis was again superior to AFP. In some cases, this type of analysis is helpful for identification of patients for whom more or less aggressive treatment and surveillance is needed

TABLE 8A Contingency table of multi-classification diagnosis in validation cohort Prediction HCC LUNC Normal HCC 329 19 6 LUNC 23 145 6 Normal 4 5 921 Undecided 89 131 191 Totals 354 174 930 Correct 329 145 921 Sensitivity (%) 92.4 85.8 Specificity (%) 99.0

TABLE 8B Contingency table of binary classification diagnosis between HCC and normal Prediction HCC Normal cancer 371 16 normal 4 921 undecided 70 187 Totals 375 937 Correct 371 921 Sensitivity (%) 98.9 Specificity (%) 98.3

TABLE 8C Contingency table of binary classification diagnosis between LUNC and normal Prediction LUNC Normal cancer 188 16 normal 5 921 undecided 107 187 Totals 193 937 Correct 188 921 Sensitivity (%) 97.4 Specificity (%) 98.3

TABLE 9 List of MCBs selected by multi-class LASSO and used for cd-score generation. A, normal; B, LUNC; C, HCC. Read Logistic regression coefficients Diagnosis MCBs counts Target ID RefGene LUNC vs HCC cancer vs normal A, Normal Normal  1-1693966 mc cg00100121 C1orf114 0.058289236 −0.460201686 non_mc cg00100121 C1orf114 −0.264294546 −0.362527983 11-474165 mc cg13912307 SLC39A13 −0.019821436 −0.342748675 non_mc cg13912307 SLC39A13 −0.119495297 −0.097830945 11-791245 mc cg08794954 ODZ4 −0.502459754 −0.119001342 non_mc cg08794954 ODZ4 −0.050537884 −0.237868292 12-561356 mc cg00344358 GDF11 0.013443025 −0.152292508 non_mc cg00344358 GDF11 0.210631385 −0.24781796 16-7128  mc cg05773599 WDR90 −0.220759943 −0.438656833 non_mc cg05773599 WDR90 0.123092944 −0.14690629 16-856199 mc cg10174683 — 0.05314766 0.335012824 non_mc cg10174683 — −0.182838798 −0.279797616 17-579157 mc cg12054453 TMEM49 0.062499156 −0.478242327 non_mc cg12054453 TMEM49 0.549358501 0.925660526 17-742484 mc cg24166450 — 0.240682421 0.067853997 non_mc cg24166450 — 0.443449771 0.566346654  2-1139315 mc cg09366118 PSD4 0.098868936 0.269196214 non_mc cg09366118 PSD4 0.039975437 −0.005683265  2-293390 mc cg21972382 CLIP4 −0.099097051 −0.61331161 non_mc cg21972382 CLIP4 0.074745373 0.2650865 22-321497 mc cg03550506 DEPDC5 −0.068645581 −0.755902786 non_mc cg03550506 DEPDC5 −0.046222783 −0.124629981  3-1138223 mc cg06722069 — −0.427422216 −0.26910555 non_mc cg06722069 — 0.089047399 −0.071525276 4-13248 mc cg07748255 MAEA 0.217866978 −0.457591409 non_mc cg07748255 MAEA −0.011474644 −0.340582424  5-1489298 mc cg22928002 CSNK1A1 −0.19633718 −0.006367323 non_mc cg22928002 CSNK1A1 −0.131881054 0.459975236  5-429520 mc cg17757602 — 0.041309712 0.029262464 non_mc cg17757602 — −0.133713183 −0.355503949  5-429521 mc cg17757602 — 0.127773182 0.169659022 non_mc cg17757602 — −0.3026458 −0.526377272  6-276491 mc cg03161803 — −0.134554024 0.040255772 non_mc cg03161803 — −0.076945207 −0.517353281  6-912971 mc cg01087382 MAP3K7 0.132083258 −0.263202543 non_mc cg01087382 MAP3K7 0.023651284 0.314783378  7-1017626 mc cg06721601 CUX1 0.305704307 0.347231675 non_mc cg06721601 CUX1 −0.061678409 0.340362292  7-1577443 mc cg27104173 PTPRN2 0.029132702 −0.207025939 non_mc cg27104173 PTPRN2 0.004420766 −0.098341178  9-885141 mc cg13740515 — 0.028390415 −0.269846773 non_mc cg13740515 — −0.208939054 −0.193859618 X-27307  mc cg13176022 XG 0.511857283 0.042177783 non_mc cg13176022 XG 0.048888339 −0.232292001 B: LUNC LUNC  1-1693966 mc cg00100121 C1orf114 0.058289236 −0.460201686 non_mc cg00100121 C1orf114 −0.264294546 −0.362527983  11-1169673 mc cg16858415 SIK3 0.374145093 0.446746019 non_mc cg16858415 SIK3 0.297361321 0.42281836 11-476248 mc cg05585544 — 0.570677409 0.329004001 non_mc cg05585544 — 0.127193074 −0.357434967 11-646423 mc cg18518074 EHD1 0.305179647 0.273679057 non_mc cg18518074 EHD1 −0.170377122 −0.416439448 11-779080 mc cg03423942 USP35 0.049737225 0.05832888 non_mc cg03423942 USP35 −0.152714594 0.278866832 11-791245 mc cg08794954 ODZ4 −0.502459754 −0.119001342 non_mc cg08794954 ODZ4 −0.050537884 −0.237868292 12-687588 mc cg20323175 — 0.148739937 0.171957693 non_mc cg20323175 — −0.249738102 0.397951832 12-72767  mc cg16959747 RBP5 0.09713964 −0.079430598 non_mc cg16959747 RBP5 0.055654584 0.2763291 13-256210 mc cg25366582 — 0.168798263 −0.174946818 non_mc cg25366582 — 0.031754087 0.303827389 16-571473 mc cg06880930 CPNE2 0.235333764 0.168616245 non_mc cg06880930 CPNE2 −0.072680275 −0.290080025 17-48361  mc cg25526759 GP1BA −0.01244657 0.090902282 non_mc cg25526759 GP1BA 0.14744695 0.344718909 17-579157 mc cg12054453 TMEM49 0.062499156 −0.478242327 non_mc cg12054453 TMEM49 0.549358501 0.925660526 17-742484 mc cg24166450 — 0.240682421 0.067853997 non_mc cg24166450 — 0.443449771 0.566346654 19-460568 mc cg27391679 OPA3 0.044319948 0.082373402 non_mc cg27391679 OPA3 −0.243746298 −0.096052603  2-293390 mc cg21972382 CLIP4 −0.099097051 −0.61331161 non_mc cg21972382 CLIP4 0.074745373 0.2650865  2-382010 mc cg20626840 FAM82A1 0.237250003 0.741677389 non_mc cg20626840 FAM82A1 0.272198397 0.04877136 2-95267 mc cg15545942 ASAP2 −0.557516889 −0.185093352 non_mc cg15545942 ASAP2 −0.11738118 −0.171014921  3-1138223 mc cg06722069 — −0.427422216 −0.26910555 non_mc cg06722069 — 0.089047399 −0.071525276  5-1767847 mc cg04466840 RGS14 −0.206022964 −0.032074773 non_mc cg04466840 RGS14 −0.029935092 0.110871368  5-429520 mc cg17757602 — 0.041309712 0.029262464 non_mc cg17757602 — −0.133713183 −0.355503949  6-1574304 mc cg17475813 ARID1B 0.129475454 0.045380298 non_mc cg17475813 ARID1B −0.398696708 −0.14398793  6-283040 mc cg08343881 ZNF323 0.002401379 0.00651448 non_mc cg08343881 ZNF323 0.255186684 0.345026825  6-352655 mc cg02919168 DEF6 −0.372390965 −0.054727228 non_mc cg02919168 DEF6 0.070785826 −0.098822441  6-912971 mc cg01087382 MAP3K7 0.132083258 −0.263202543 non_mc cg01087382 MAP3K7 0.023651284 0.314783378  7-1017626 mc cg06721601 CUX1 0.305704307 0.347231675 non_mc cg06721601 CUX1 −0.061678409 0.340362292  7-1228399 mc cg22024657 SLC13A1 0.013826947 0.222664259 non_mc cg22024657 SLC13A1 −0.056854833 0.037841165  8-1025044 mc cg18004756 GRHL2 −0.293288901 0.034342458 non_mc cg18004756 GRHL2 −0.043777572 −0.203017346  8-1444164 mc cg12188860 TOP1MT 0.094522888 −0.083700307 non_mc cg12188860 TOP1MT −0.083425117 −0.026557506 C: HCC HCC  1-1695560 mc cg16054275 F5 −0.214291458 −0.606905543 non_mc cg16054275 F5 0.524710881 −0.648261504  1-2035950 mc cg06637618 ATP2B4 −0.198233007 −0.368854771 non_mc cg06637618 ATP2B4 0.023774113 0.157886336  1-2130901 mc cg04607844 — −0.293894301 −0.012727778 non_mc cg04607844 — 0.119395918 0.246150856 10-80958  mc cg18187680 FLJ45983 −0.028534778 0.369256194 non_mc cg18187680 FLJ45983 −0.130812947 −0.247877838  12-1222773 mc cg26386472 HPD −0.472436655 0.005424793 non_mc cg26386472 HPD 0.131065837 −0.084249928 15-555695 mc cg02712036 RAB27A −0.178526212 0.136059681 non_mc cg02712036 RAB27A −0.136916211 0.13904572 16-724595 mc cg07864976 — 0.159138061 0.220936294 non_mc cg07864976 — −0.174358026 0.041182013 17-579157 mc cg12054453 TMEM49 0.062499156 −0.478242327 non_mc cg12054453 TMEM49 0.549358501 0.925660526 17-800195 mc cg20651080 DUS1L 0.16564247 0.179098878 non_mc cg20651080 DUS1L −0.072821844 0.405200855 17-803588 mc cg11252953 C17orf101 0.009872935 0.197906181 non_mc cg11252953 C17orf101 −0.088619833 −0.047902639 19-459097 mc cg06663668 CD3EAP 0.026258194 0.077247979 non_mc cg06663668 CD3EAP −0.209537326 0.55413999 19-546460 mc cg11441617 CNOT3 −0.272150647 0.31583956 non_mc cg11441617 CNOT3 −0.485831577 0.011388271 19-546461 mc cg11441617 CNOT3 0.345456601 0.601027916 non_mc cg11441617 CNOT3 0.252775143 0.151478291  2-1139315 mc cg09366118 PSD4 0.098868936 0.269196214 non_mc cg09366118 PSD4 0.039975437 −0.005683265 21-364214 mc cg01519261 RUNX1 0.350276252 0.086780025 non_mc cg01519261 RUNX1 0.205448988 −0.230292808 21-364215 mc cg01519261 RUNX1 −0.068910744 0.175787941 non_mc cg01519261 RUNX1 −0.470630509 −0.686309747 22-185277 mc cg02415779 — 0.056697461 0.313717112 non_mc cg02415779 — 0.122239689 −0.243806777 22-378130 mc cg00107982 ELFN2 −0.03021978 0.143360514 non_mc cg00107982 ELFN2 −0.063348947 0.490504707 4-13248 mc cg07748255 MAEA 0.217866978 −0.457591409 non_mc cg07748255 MAEA −0.011474644 −0.340582424  4-840359 mc cg19255783 PLAC8 0.024851345 0.211399591 non_mc cg19255783 PLAC8 −0.154998544 −0.217029094   5-17153 85 mc cg25650256 STK10 −0.296078256 0.069437129 non_mc cg25650256 STK10 −0.134013643 0.178025221  6-262504 mc cg05414338 HIST1H3F −0.291906307 0.385669376 non_mc cg05414338 HIST1H3F −0.188285503 0.279553121  6-315278 mc cg06393830 NFKBIL1 0.120351939 0.122094253 non_mc cg06393830 NFKBIL1 0.206309569 −0.082505936  6-329093 mc cg00862588 HLA-DMB −0.322857861 0.005678509 non_mc cg00862588 HLA-DMB 0.382920486 −0.054366936  6-912971 mc cg01087382 MAP3K7 0.132083258 −0.263202543 non_mc cg01087382 MAP3K7 0.023651284 0.314783378  7-1000913 mc cg03113878 C7orf51 −0.173651065 −0.291203774 non_mc cg03113878 C7orf51 −0.009576582 0.090427416  7-759325 mc cg21217886 HSPB1 −0.135144273 −0.558395663 non_mc cg21217886 HSPB1 −0.163997602 −0.133445148

TABLE 10 Characteristics of 10 MCBs in LUNC prognosis prediction and 10 MCBs in HCC prognosis prediction Features Target ID RefGene LUNC mc_17-742484 cg24166450 — non_mc_2-741532 cg02478828 DGUOK mc_2-2355288 cg08436738 — mc_1-295863 cg04933208 PTPRU non_mc_22-358223 cg20146967 MCM5 non_mc_16-900927 cg07860918 GAS8 mc_20-374337 cg16119522 PPP1R16B non_mc_3-1960650 cg05556202 TM4SF19 mc_12-687588 cg20323175 — non_mc_10-299484 cg13324103 SVIL HCC mc_6-262503 cg05414338 HIST1H3F mc_6-733300 cg17126142 KCNQ5 non_mc_7-450187 cg06787669 MYO1G mc_19-185898 cg06747543 ELL mc_12-1222773 cg26386472 HPD non_mc_1-20665 cg00866690 PRKCZ mc_12-939663 cg11225410 SOCS2 mc_7-450187 cg06787669 MYO1G mc_6-283040 cg08343881 ZNF323 mc_6-733299 cg17126142 KCNQ5

TABLE 11 Clinical characteristics of study cohort TCGA TCGA HCC LUNC GSE HCC LUNC Normal Characteristic tissue tissue Normal blood blood blood Total (n) 377  827  754 1504   892  2247  Gender Female-no. (%) 122 (32.4) 340 (41.1) 401 (53.2) 146 (9.7) 263 (29.5) 507 (22.6) Male-no. (%) 255 (67.6) 487 (58.9) 353 (46.8) 991 (65.9) 487 (54.6) 480 (21.4) NA 0 0 0 367 (24.4) 142 (15.9) 1260 (56.1) Age (years) Mean 61  68  63 54  58  48 Range 16-90 33-90 19-101 11-85 19-85 19-90 Pathology Hepatocellular 367 (97.3) 0 NA 1504 (100) 0 NA carcinoma(%) Adenocarcinoma(%) 0 458 (55.4) NA 0 402 (45.1) NA Squamous cell 0 369 (44.6) NA 0 138 (15.5) NA carcinoma(%) Small Cell Lung 0 0 NA 0 79 (8.9) NA Cancer(%) Others(%) 10 (2.7) 0 NA 0 273 (30.6) NA Stage I (%) 175 (46.4) 424 (51.3) NA 206 (13.7) 58 (6.5) NA II (%) 87 (23.1) 115 (13.9) NA 202 (13.4) 52 (5.8) NA III (%) 86 (22.8) 261 (31.6) NA 612 (40.7) 148 (16.6) NA IV (%) 6 (1.6) 25 (3.0) NA 134 (8.9) 463 (51.9) NA NA (%) 23 (6.1) 2 (0.2) NA 350 (23.3) 171 (19.2) NA Tumor burden Tumor free (%) 236 (62.6) 503 (60.8) NA 314 (20.9) 160 (17.9) NA With tumor (%) 114 (30.2) 159 (19.2) NA 889 (59.1) 599 (67.2) NA NA (%) 27 (7.2) 165 (20.0) NA 301 (20.0) 133 (14.9) NA EGFR status Wide type (%) NA 400 (48.4) NA NA 102 (11.4) NA Mutation (%) NA 100 (12.1) NA NA 69 (7.7) NA NA (%) NA 327 (39.5) NA NA 721 (80.8) NA Hepatitis Positive (%) 120 (31.8) NA NA 623 (95.3) NA 379 (16.9) Negative (%) 119 (31.6) NA NA 10 (1.5) NA 571 (25.4) NA (%) 138 (36.6) NA NA 21 (3.2) NA 1297 (57.7) Smoking (%) Current smoker (%) NA 725 (87.7) NA NA NA 192 (8.5) Non-smoker (%) NA 80 (9.7) NA NA NA 670 (29.8) NA (%) NA 22 (2.6) NA NA NA 1385 (61.6)

TABLE 12 Multivariate survival analysis for HCC patients and LUNC patients with composite-score of methylation markers (cp-score) and relevant variables in validation cohorts coef exp(coef) se(coef) z p lower 0.95 upper 0.95 HCC cp-score 0.9115 2.4881 0.2696 3.3813 0.0007 0.3830 1.4400 stage 0.3336 1.3960 0.3034 1.0995 0.2715 −0.2612 0.9284 AFP −0.0861 0.9175 0.2115 −0.4069 0.6841 −0.5008 0.3286 age −330.8028 0.0000 75.7050 −4.3696 0.0000 −479.2145 −182.3911 Gender −0.0885 0.9153 0.2361 −0.3748 0.7078 −0.5512 0.3743 LUNC cp-score 0.5577 1.7467 0.3056 1.8249 0.0680 −0.0414 1.1569 stage 0.4647 1.5916 0.8422 0.5518 0.5811 −1.1863 2.1157 CEA 0.2695 1.3093 0.6035 0.4466 0.6552 −0.9135 1.4525 age −321.0835 0.0000 94.6796 −3.3913 0.0007 −506.6928 −135.4742 Gender 0.0897 1.0938 0.3858 0.2325 0.8162 −0.6666 0.8460

TABLE 13 Clinical characteristics and sensitivity/specificity for detection of stage I LUNC and benign lung nodules Sensitivity and Specificity for the Detection of Lung Cancer Stage I lung cancer Benign nodules Characteristic N = 116 N = 116 Age-yr Median 61 52 Range 29-83 26-86 Gender Female 80 53 male 36 63 Pathology Adenocarcinoma 100 NA Squamous cell carcinoma 6 NA Small Cell Lung Cancer 0 NA Others 0 NA Nodule size-mm Mean 14.6 ± 6.1 7.2 ± 2.1 Median 15.0   6.4 Range  3-58  2.7-18.2 Stage AIS 7 NA MIA 8 NA IA 78 NA IB 13 NA Sensitivity - % (95% CI) 77.8 Specificity - % (95% CI) 85.3 Positive predictive value - 81.0 % (95% CI) Negative predictive value - 84.1 % (95% CI)

TABLE 14 Clinical characteristics and sensitivity/specificity for detection of progression to stage I HCC from liver cirrhosis Sensitivity and Specificity for the Detection of Liver Cancer Stage I liver cancer Cirrhosis Characteristic N = 204 N = 242 Age-yr Median 62 52 Range 21-81 25-82 Gender Female 32 47 Male 172 195 Hepatitis B Positive 198 193 Negative 4 13 NA 2 36 AFP <25 ng/ml 113 196 >25 43 29 NA 48 17 Sensitivity - % (95% CI) 94.9 Specificity - % (95% CI) 92.6 Positive predictive value - 94.9 % (95% CI) Negative predictive value - 92.8 % (95% CI)

Example 3

Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths worldwide. As with many cancers. HCC found at an early stage carries much-improved prognosis compared to advanced stage disease, in part due to the relative efficacy of local treatments compared with systemic therapy, lit some cases, early detection has a potential for reducing the mortality of HCC. In some instances, alpha fetal protein (AFP) is used for detection and surveillance of HCC. However, it some cases its clinical utility is limited by low sensitivity.

DNA methylation is an epigenetic regulator of gene expression that usually results in gene silencing. In cancer, DNA methylation is typically increased in tumor suppressor genes and presents itself as one of the first neoplastic changes. Circulating tumor DNA (ctDNA) comprises of extracellular nucleic acid fragments shed into plasma via tumor cell necrosis, apoptosis, and active release of DNA. In some cases, ctDNA hearing cancer-specific methylation patterns is used as a biomarker in diagnosis of cancers.

Patient Data

Tissue DNA methylation data was obtained from The Cancer Genome Atlas (TCGA). Complete clinical, molecular, and histopathological datasets are available at the TCGA website. Individual institutions that contributed samples coordinated the consent process and obtained informed written consent from each patient in accordance to their respective institutional review boards.

A second independent Chinese cohort consisted of HCC patients at the Sun Yat-sen University Cancer Center in Guangzhou, Xijing Hospital in Xi'an and the West China Hospital in Chengdu, China. Those who presented with HCC from stage I-IV were selected and enrolled in this study. Patient characteristics and tumor features are summarized in Table 17. The TNM staging classification for HCC is according to the NCCN Hepatobiliary Cancers Clinical Practice Guidelines (Benson III A B, Abrams T A, Ben-Josef E et al. Hepatobiliary Cancers: Clinical Practice Guidelines in Oncology, Journal of the National Comprehensive Cancer Network: JNCCN 2009; 7: 350). This project was approved by the IRBs of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. Informed consent was obtained from all patients. Tumor and normal tissues were obtained as clinically indicated for patient care and were retained for this study. Human blood samples were collected by venipuncture and plasma samples were obtained by taking supernatant after centrifugation and stored at −80° C. before cfDNA extraction.

Data Sources

DNA methylation data of 485,000 sites generated using the Infinium 450K Methylation Array were obtained from the TCGA and dataset generated from the previous study (GSE40279) in which DNA methylation profiles fix HCC and blood were analyzed. IDAT format files of the methylation data were generated containing the ratio values of each scanned bead. Using the minfi package from Bioconductor, these data files were converted into a score, referred to as a Beta value Methylation values of the Chinese cohort were obtained by targeted bisulfate sequencing using a molecular inversion probe and analyzed as described below.

Statistical Analysis—DNA Methylation Marker Pre-Selection for Diagnostic and Prognostic Analysis

A differential methylation analysis on TCGA data using a “moderated t-statistics shrinking” approach was first performed and the P-value for each marker was then corrected by multiple testing by the Benjamini-Hochberg procedure to control FDR at a significance level of 0.05. The list was ranked by adjusted P-value and selected the top 1000 markers for designing padlock probes (FIG. 26). About 682 padlock probes were obtained that gave positive and specific PCR amplification signals and were used as capture probes in the subsequent experiments in cfDNA samples (FIG. 27A-FIG. 27H). cfDNA samples with low quality or fewer than 20,000 reads per sample were also eliminated. About 1933 cfDNA samples were included in the study (1098 HCC blood samples and 835 normal blood samples). Methylation values for each marker were defined as the proportion of read counts with methylation divided by total read counts. Of the 682 padlock probes, about 401 markers were retained for further analysis after eliminating methylation markers with a range of methylation values less than 0.1 in matched tumor tissue and rumor blood samples (FIG. 27A). For a particular methylation markers with less than 20 unique reads, an imputed mean methylation value of HCC or normal healthy controls were used.

Building a Diagnostic Model

The full cfDNA dataset (1098 HCC blood samples and 835 normal blood samples) was randomly split into training and validation cohorts with a 2:1 ratio, corresponding to 1275 and 658 total samples, respectively. Two variable selection methods suitable for high-dimensionality on the prescreened training dataset were applied: Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest based variable selection method using OOB error. As results can depend strongly on the arbitrary choice of a random sample split for sparse high-dimensional data, an analysis of the “multi-split” method were adopted, which improves variable selection consistency while controlling finite sample error. For LASSO selection operator, 75 percent of the dataset was subsampled without replacement 500 times and selected the markers with repeat occurrence frequency more than 450. The tuning parameters was determined according to the expected generalization error estimated from 10-fold cross-validation and information-based criteria AIC/BIC, and we adopted the largest value of lambda such that the error was within one standard error of the minimum, known as “1-se” lambda. For the random forest analysis, using the OOB error as a minimization criterion, variable elimination from the random forest was carried out by setting variable a dropping traction of each iteration at 0.3. Ten overlapping methylation markers were chosen by the two methods for model building a binary prediction. A logistic regression model was fitted using these 10 markers as the covariates and obtained a combined diagnosis score (designated as cd-score) by multiplying the unbiased coefficient estimates and the marker methylation value matrix in both the training and validation datasets. The predictability of the model was evaluated by area under ROC (AUC, also known as C-index), which calculated the proportions of concordant pairs among all pairs of observations with 1.0 indicating a perfect prediction accuracy. Confusion tables were generated using an optimizes cd-score cutoff with a maximum Youden's index.

The pre-treatment or initial methylation level was evaluated at baseline, and the post-methylation level was evaluated approximately 2 months after treatment, where the treatment referred to either chemotherapy or surgical resection of tumor. The primary endpoint (including response to treatment: progressive disease (PD), partial response (PR) and stable disease (SD)) were defined according to the RECIST guideline. For patients treated with surgical removal and no recurrence at time of evaluation, it was assumed that they had complete response (CR). The difference of cd-score distribution between clinical categories was examined by Wilcoxon Rank Sum test as cd-score was tested to be non-normally distributed using a Shapiro-Wilk Test.

Building a Predictive Model for Prognosis and Survival

The HCC cohort was split into a training dataset and a validation dataset and explored building a predictive model for prognosis and survival. About 680 cases were used for training and 369 cases for validation. Next, in the training dataset, a sequential model-based variable selection strategy was applied to screen markers for predicting survival outcome. A univariate pre-screening procedure was first performed to remove excessive noise to facilitate the computational analysis, which is generally recommended prior to applying any variable selection method. For each methylation marker, a univariate Cox proportional hazards model was fit by using each marker as the covariate. A marker with p-value <0.05 from the Wald statistic was retained in the dataset. Second, a similar subsampling strategy was used in a diagnosis marker selecting process based on LASSO-cox method to shrink the marker numbers to a reasonable range (less than events). Slightly different from the binary classifier, subsampling in the training dataset with replacement, in case that the event proportion was too low for a model construction. The frequency cutoff was set as 50 to retain approximately 1/10th of total events. The above analysis generated 8 final markers to construct a prognostic signature. By fitting a multi-variable Cox proportional hazards model on these 8 markers, the coefficients of each marker was determined and a combined prognostic score (designated as cp-score) was obtained for each individual. To validate the predictive model, a cp-score was calculated for each patient in the validation dataset using coefficient estimates from the training dataset. By dividing the cp-score according to its median, high and low cp-score groups were formed with a roughly equal number of observations. It was investigated if the median survival time was significantly different between these two groups using a Kaplan-Meier estimator and log-rank test.

Using cp-score and clinical information as covariates, multi-variable Cox proportional hazards models were fitted separately in the training and validation dataset to infer weather the cp-score is an independent predictive factor when compared to AFP, stage, sex and age for HCC prognosis.

All the hypothesis testing is two-sided with p-value <0.05 considered to be statistically significant. All the analysis was conducted in R version 3.2.3 with the following packages used: ‘glmnet’, ‘rms’, ‘pROC’, ‘limma’, ‘ROCR’, ‘varSelRF’, ‘survival’.

Tumor DNA Extraction

Genomic DNA extraction from freshly frozen healthy or cancer tissues was performed with QIAamp DNA Mini Kit (Qiagen) according to manufacturer's recommendations. Roughly 0.5 mg of tissue was used to obtain on average 5 μg of genomic DNA. DNA was stored at −20° C., and analyzed within one week of preparation.

DNA Extraction from FFPE Samples

Genomic DNA from frozen FFPE samples was extracted using QIAamp DNA FFPE Tissue Kit with several modifications. DNA were stored at −20° C. for further analysis.

Cell-Free DNA Extraction from Plasma Samples

cfDNA extraction from 1.5 ml of plasma samples was performed with QIAamp cfDNA Kit (Qiagen) according to manufacturer's recommendations.

The minimal volume of plasma that will give a consistent cfDNA recovery and reliable sequencing coverage defined as more than 20 reads for a target cg marker was investigated. It was shown that a requirement of at least 20,000 total reads per sample corresponded to 20 or more reads per cg marker for ˜90% of markers in a sample and was accurate to make a good prediction of the beta value of the corresponding cg marker. It was found that 1.5 nil or more plasma can give 20 or more reads per cg marker for ˜90% of markers in a sample. The relationship between amount of cfDNA in 1.5 ml plasma and copy numbers was further investigated using digital droplet PCR. It was found that DNA quantity of 15 ng gave good copy numbers as defined by >140 total copies of detected amplicons in each digital droplet PCR assay (FIG. 39B) and a minimal 1.5 ml plasma volume gave a cfDNA yield of 15 ng. About 15 ng/1.5 ml as a cutoff was used in the experiments to obtain consistent and reliable measurements of DNA quantity and good recovery. cfDNA yield was also compared by several commercial cfDNA extraction kits (EliteHealth, Qiagen and Thermo Fisher). It was found that EliteHealth or Qiagen cfDNA extraction kits yielded the highest cfDNA quantities in a highly consistent manner [˜10 ng+/−3 ng/per 1 ml plasma, FIG. 39C].

Bisulfite Conversion of Genomic DNA

About 10-15 ng of cfDNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according Co the manufacturer's protocol. Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfite conversion was >99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CM (non-CG) dinucleotides.

Determination of DNA Methylation Levels by Deep Sequencing of Bis-DNA Captured with Molecular-Inversion (Padlock) Probes

CpG markers whose methylation levels significantly differed in any of the comparisons between any cancer tissue and any normal tissue were used to design padlock probes for capture and sequencing of cfDNA. Padlock-capture of bis-DNA was based on the technique on published methods with modifications.

Probe Design and Synthesis

Padlock probes were designed using the ppDesigner software. The average length of the captured region was 100 bp, with the CpG marker located in the central portion of the captured region. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produced probes of equal length. A 6-bp unique molecular identifier (UMI) sequence was incorporated into the probe design to allow for the identification of unique individual molecular capture events and accurate scoring of DNA methylation levels. See Table 19.

Probes were synthesized as separate oligonucleotides using standard commercial synthesis methods (ITD). For capture experiments, probes were mixed, in vitro phosphorylated with T4 PNK (NEB) according to manufacturer's recommendations and purified using P-30 Micro Bio-Spin columns (Bio-Rad).

Bis-DNA Capture

About 10 ng of bisulfite-converted DNA was mixed with padlock probes in 20 μl reactions containing 1× Ampligase buffer (Epicentre). To anneal probes to DNA, 30 second denaturation at 95° C. was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second. Hybridization was left to complete for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2 U of PfuTurboCx polymerase (Agilent), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in 1× Ampligase buffer. After 5 hour incubation at 55° C. reactions were denatured for 2 minutes at 94° C. 5 μl of exonuclease mix (20 U of Exo I and 100 U of ExoIII, both from Epicentre) was added and single-stranded DNA degradation was carried our at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C.

Circular products of site-specific capture were amplified by PCR with concomitant barcoding of separate samples. Amplification was carried out using primers specific to linker DNA within padlock probes, one of which contained specific 6 bp barcodes. Both primers contained Illumina next-generation sequencing adaptor sequences. PCR was done as follows: 1× Phusion Flash Master Mix, 3 μl of captured DNA and 200 nM primers, using the following cycle: 10 s at 98° C., 8× of (1 s at 98° C., 5 s at 58° C., 10 s at 72° C.), 25× of (1 s at 98° C. 15 s at 72° C.), 60 s at 72° C. PCR reactions were mixed and the resulting library was size selected to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp) using Agencourt AMPure XP beads (Beckman Coulter). Purity of the libraries was verified by PCR using Illumina flowcell adaptor primers (P5 and P7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries we sequenced using MiSeq and HiSeq2500 systems (Illumina).

Optimization of Capture Coverage Uniformity

Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60-65% of captured regions with coverage >0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at >0.2× of average coverage.

Sequencing Data Analysis

Mapping of sequencing reads was done using the software tool bisReadMapper with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script. Reads were on-the-fly converted as if all C were non-methylated and mapped to in-silico converted DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2. Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single, unique read. Methylation frequencies were calculated for all CpG dinucleotides contained within the regions captured by padlock probes by dividing the numbers of unique reads carrying a C at the interrogated position by the total number of reads covering the interrogated position.

Identification of Blocks of Correlated Methylation (BCM)

Pearson correlation coefficients between methylation frequencies of each pair of CpG markers separated by no more than 200 bp were calculated separately across 50 cfDNA samples from each of the two diagnostic categories, ie normal health blood and HCC. A value of Pearson's r<0.5 was used to identify transition spots (boundaries) between any two adjacent markers indicating uncorrelated methylation. Markers not separated by a boundary were combined into Blocks of Correlated Methylation (BCM). This procedure identified a total of ˜1350 BCM in each diagnostic category within our padlock data, combining between 2 and 22 CpG positions in each block. Methylation frequencies for entire BCMs were calculated by summing up the numbers of Cs at ail interrogated CpG positions within a BCM and dividing by the total number of C+Ts at those positions

DNA Isolation and Digital Quantitative PCR

Tumor and corresponding plasma samples were obtained from patients undergoing surgical tumor resection; samples were frozen and preserved in at −80° C. until use. Isolation of DNA and RNA from samples was performed using AllPrep DNA/RNA Mini kit and a cfDNA extraction kit, respectively (Qiagen, Valencia, Calif.).

To estimate tumor cfDNA fractions, mixing experiments with various fractions of normal cfDNA and HCC tumor genomic DNA (gDNA) were performed and methylation values and copy numbers were assayed by digital droplet PCR (ddPCR, see next section for details). ddPCR was performed according to the manufacturer's specifications (Bio-Rad, Hercules, Calif.). ddPCR was set up as following: amplification primer pair, cg10590292-Forward 5′-TGTTAGTTTTTATGGAAGTTT, cg10590292-Reverse 5-AAACIAACAAAATACTCAAA; fluorescent probe for methylated allele detection, cg10590292-M 5′/6-FAM/TGGGAGAGCGGGAGAT/BHQ1/-3′; probe for unmethylated allele detection, cg10590292-NM 5′/HEX/TTTGGGAGAGTGGGAGATTT/BHQ1/-3′. Cycling condition: 1× of 10 ruins at 98° C., 40× of (30 s at 98° C. 60 s at 53° C.), 1× of 10 mins at 98° C.

Calculation of Tumor cfDNA Fraction

It was assumed that a particular methylation value observed for an HCC cfDNA sample resulted from the combined contribution of normal and tumor cfDNA. The fraction of cfDNA originating from the tumor was estimated using the following formula: traction contributed from tumor DNA in sample i=[methylation value in HCC cfDNA in sample i−mean methylation value of normal ctDNA]/[mean methylation value of tumor DNA−mean methylation value of normal cfDNA]. Using this approach, it was estimated that on average the tumor fraction was about 23% in HCC cfDNA samples. Samples were then grouped according to factors that evaluate tumor load, such as an advanced stage and pre-treatment status, since these factors were expected to affect the tumor fraction in ctDNA (Table 20). Indeed, it was observed that conditions associated with a higher tumor staging and severity also tended to have a larger tumor fraction (Table 20). To further vet this approach, a mixing experiment was performed with different fractions of normal cfDNA (0-100%) and tumor genomic DNA (0-100%) and assayed methylation values using digital PCR. It was shown that incremental addition of tumor genomic DNA could increase methylation fraction percentage up to the values observed in the HCC patient samples (FIG. 38A-FIG. 38C). Specifically, addition of 10%, 20%, 40%, 60% or 100% fraction of tumor genomic DNA could be predicted by the above formula, when using methylation values obtained from the experiment.

cfDNA concentration was measured in normal and HCC plasma samples using a Qubit® fluorescent dye method 10.1 ul out of 20 ul of cfDNA extracted from a 1.5 mL plasma sample was diluted in 199 ul Qubit® working solution containing Qubit® dsDNA HS Reagent. It was found that on average, there are about 11 ng cfDNA in 1 mL of normal plasma and 22 ng cfDNA in 1 mL of HCC plasma (FIG. 39A).

Patient and Sample Characteristics

Clinical characteristics and molecular profiling including methylation data for comparison between HCC and blood lymphocytes including 377 HCC tumor samples from The Cancer Genome Atlas (TCGA) and 754 normal samples from a dataset used in a methylation study on aging (GSE40279). To study ctDNA in HCC, plasma samples were obtained from Chinese patients with HCC and randomly selected healthy controls undergoing routine health care maintenance, resulting in a training cohort of 715 HCC patients and 560 normal healthy controls and a validation cohort of 383 HCC patients and 275 healthy controls. All participants provided written informed consent. Clinical characteristics of all patients and controls are listed in Table 17.

Identification of Methylation Markers Differentiating HCC and Blood

It is hypothesized that CpG markers with a maximal difference in methylation between HCC and normal blood is likely to demonstrate detectable methylation differences in the cfDNA of HCC patients when compared to that of normal controls. The “moderate t-statistics” method with Empirical Bayes for shrinking the variance was used and Benjamini-Hochberg procedure was used to control the FDR at a significance level of 0.05 to get top 1000 markers with the lowest P values, identifying the most significantly different rates of methylation between HCC and blood (FIG. 26). Unsupervised hierarchical clustering of these top 1000 markers was able to distinguished between HCC and normal blood (FIG. 30). Molecular inversion (padlock) probes corresponding to these 1000 markers were designed and tested in 28 pairs of HCC tissue DNA and matched plasma ctDNA within the same patient. The methylation profiles in HCC tumor DNA and matched plasma ctDNA were highly consistent, giving them potential as sensitive diagnostic markers (FIG. 27A, FIG. 27B, FIG. 34A, and FIG. 34B). 401 markers demonstrated a good amplification profile and dynamic methylation value and were therefore selected as good candidates for further analysts.

Methylation Block Structure for Improved Allele Calling Accuracy

The concept of genetic linkage disequilibrium (LD block) was used to study the degree of co-methylation among different DNA stands. Paired-end Illumina sequencing reads were used to identify each individual methylation block (mBlock). A Pearson correlation method was applied to quantify co-methylation or mBlock. All common mBlocks of a region were compiled by calculating different mBlock fractions. Next, the genome was partitioned into blocks of tightly co-methylated CpG sites termed methylation correlated block (MCB), using an r² cutoff of 0.5. MCBs were surveyed in cfDNA of 500 normal samples and it was found that MCBs are consistent. Methylation levels within an MCB in cfDNA in 500 HCC samples were then determined. It was found that an MCB has a consistent methylation pattern when comparing normal versus HCCcfDNA samples (FIG. 31A-FIG. 31B).

ctDNA Diagnostic Prediction Model for HCC

The methylation values of the 401 selected markers that showed good methylation ranges in cfDNA samples were analyzed by Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods to further reduce the number of markers by modeling them in 715 HCC ctDNA and 560 normal ctDNA samples (FIG. 26). About 24 markers were obtained using the Random Forest analysis. 30 markers were obtained using a LASSO analysis in which it was required that selected markers were to appear over 450 times out of a total of 500 repetitions. There were 10 overlapping markers between these two methods (Table 15). Using a logistic regression method, a diagnostic prediction model was constructed with these 10 markers. Applying the model yielded a sensitivity of 85.7% and specificity of 94.3% for HCC in the training dataset of 715 HCC and 560 normal samples (FIG. 27C) and a sensitivity of 83.2% and specificity of 90.5% in the validation dataset of 383 HCC and 275 normal samples (FIG. 27D). In some cases, this model further differentiate HCC from normal controls both in the training dataset (AUC=0.966) and the validation dataset (AUC=0.944) (FIG. 27E and FIG. 27F). Unsupervised hierarchical clustering of these 10 markers was able to distinguish HCC from normal controls with high specificity and sensitivity (FIG. 27G, FIG. 27H, and FIG. 35).

A combined diagnostic score (cd-score) of the model was assessed for differentiating between liver diseases (e.g., HBV/HCV infection, cirrhosis, and fatty liver) and HCC since these liver diseases are known risk factors for HCC. It was found that the cd-score was able to differentiate HCC patients as compared to those with liver diseases or healthy controls (FIG. 28A). These results were consistent and comparable with those predicted by AFP levels (FIG. 28B).

Methylation Markers Predicted Tumor Load, Treatment Response and Staging

The utility of the cd-score in assessing treatment response, the presence of residual tumor following treatment, and staging of HCC was further studied. Clinical and demographic characteristics, such as age, gender, race, and AJCC stage were included in the analysis. The cd-scores of patients with detectable residual tumor following treatment (n=828) were significantly higher than those with no detectable tumor (n=270), and both were significantly greater than normal controls (n=835) (p<0.0001, FIG. 28C). Similarly, the cd-scores were significantly higher in patients before treatment (n=109) or with progression (n=381) compared to those with treatment response (n=248) (p<0.0001, FIG. 28D). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=170) compared with those before surgery (n=109), yet became higher in patients with recurrence (n=155) (p<0.0001, FIG. 28E). Furthermore, there is good correlation between the cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p<0.05, FIG. 28F). Collectively, these results suggest that the cd-score (i.e., the amount of ctDNA in plasma) correlates well with tumor burden and may have utility in predicting tumor response and surveillance for recurrence.

Utility of ctDNA Diagnostic Prediction Model and AFP

In some instances, the blood biomarker for risk assessment and surveillance of HCC is serum AFP levels. However, its low sensitivity makes it inadequate to detect all patients that will develop HCC and severely limits its clinical utility. In some cases, many cirrhotic patients develop HCC without any increase in AFP levels. In additional cases, 40% patients of the HCC study cohort have a normal AFP value (<25 ng/ml).

In biopsy-proven HCC patients, the cd-score demonstrated superior sensitivity and specificity than AFP for HCC diagnosis (AUC 0.969 vs 0.816, FIG. 28G). In patients with treatment response, tumor recurrence, or progression, cd-score showed more significant changes compared to testing at initial diagnosis than AFP (FIG. 28H and FIG. 28I). In patients with serial samples, those with a positive treatment response had a concomitant significant decrease in cd-score compared to that prior to treatment, and there was an even further reduction in patients after surgery. In contrast, the patients with progressive or recurrent disease all bad an increase in cd-score (FIG. 32A-FIG. 32B). By comparison, AFP is less sensitive for assessing treatment efficacy in individual patients (FIG. 33 and FIG. 36).

In addition, cd-score correlated well with tumor stage (FIG. 28J), particularly among patients with stage I, II and III, whereas there was not significant different AFP values in patients with different stages except between patients with stage III and IV (FIG. 28K), indicating an advantage of cd-score over AFP in differentiation of early stage HCC.

ctDNA Prognostic Prediction Model for HCC

The potential to use methylation markers in ctDNA for prediction of prognosis in HCC in combination with clinical and demographic characteristics including age, gender, race, and AJCC stage was investigated. The 1049 HCC patients with complete survival information was randomly split into training and validation datasets with allocation of 2:1. The Unicox and LASSO-cox methods were implemented to reduce the dimensionality and constructed a cox-model to predict prognosis with an 8-marker panel (Table 16). Kaplan-Meier curves were generated in training and validation datasets using a combined prognosis score (cp-score) with these markers. The high-risk group (cp-score>−0.24) has 341 observations with 53 events in training dataset and 197 observations with 26 events in validation dataset; and the low-risk group (cp-score≤−0.241 has 339 observations with 7 events in training dataset and 172 observations with 9 events in validation dataset. Median survival was significantly different in both the training set (p<0.0001) and the validation set (p=0.0014) by log-rank test (FIG. 29A and FIG. 29B).

Multivariate variable analysis showed that the cp-score significantly correlated with risk of death both in the training and validation dataset, and that the cp-score was an independent risk factor of survival (hazard ratio [HR]: 2.512; 95% confidence interval [CI]: 1.966-3.210; p<0.001 in training set; HR: 1.553, CI: 1.240-1.944; p<0.001 in validation set, Table 18). Interestingly, AFP was no longer significant as a risk factor when cp-score and other clinical characteristics were taken in to account (Table 18).

TNM stage predicted the prognosis of patients in the training and validation dataset (FIG. 29C, FIG. 29D, FIG. 37A, and FIG. 37B). However, the combination of cp-score and TNM staging improved the ability to predict prognosis in both the training (AUC 0.7935) and validation datasets (AUC 0.7586). Kaplan-Meier curves also showed that patients separated by both cp-score and staging have significantly different prognosis (p<0.0001, FIG. 29G).

Table 15 illustrates characteristics of ten methylation markers and their coefficients in HCC diagnosis.

Markers Ref Gene Coefficients SE z value p value 15.595 2.395 6.513 <0.001 cg10428836 BMPR1A 11.543 0.885 −13.040 <0.001 cg26668608 PSD 4.557 0.889 5.129 <0.001 cg25754195 ARHGAP25 2.519 0.722 3.487 <0.001 cg05205842 KLF3 −3.612 0.954 −3.785 <0.001 cg11606215 PLAC8 6.865 1.095 6.271 <0.001 cg24067911 ATXN1 −5.439 0.868 −6.265 <0.001 cg18196829 Chr 6: 170 −9.078 1.355 −6.698 <0.001 cg23211949 Chr 6: 3 −5.209 1.081 −4.819 <0.001 cg17213048 ATAD2 6.660 1.422 4.683 <0.001 cg25459300 Chr 8: 20 1.994 1.029 1.938 <0.053 SE: standard errors of coefficients; z value: Wald z-static value

Table 16 illustrates the characteristics of eight methylation markers and their coefficients in HCC prognosis prediction.

Markers Ref Gene Coefficients HR CI SE z value p value cg23461741 SH3PXD2A −1.264 0.282 0.024-3.340 1.2604 −1.003 0.316 cg06482904 C11orf9 −0.247 0.781 0.067-9.100 1.2530 −0.197 0.844 cg25574765 PPFIA1 1.026 2.790  0.488-15.900 0.8894 1.153 0.249 cg07459019 Chr 17:78 −8.156 0.000 0.000-0.012 1.9112 −4.267 <0.001 cg20490031 SERPINB5 6.082 438.000   13.200-14600.000 1.7885 3.400 0.001 cg01643250 NOTCH3 −5.368 0.005 0.000-0.140 1.7357 −3.093 0.002 cg11397370 GRHL2 1.497 4.470  1.030-19.400 0.7506 1.994 0.046 cg11825899 TMEM8B 2.094 8.120  0.957-68.900 1.0909 1.920 0.055 HR: Hazard Ratio; CI: 95% confidence interval; SE: standard errors of coefficients; z value: Wald z-static value

Table 17 shows the clinical characteristics of study cohort.

HCC Normal Study Study Tissue blood HCC normal Characteristic (TCGA) (GSE) cohort cohort Total (n) 377 754 1098 835 Gender Female-no.(%) 123 (32.6) 401 (53.2) 130 (11.8) 407 (48.7) Male-no.(%) 254 (67.4) 353 (46.8) 905 (82.4) 417 (49.9) NA 0 61 (5.7) 11 (1.4) Age (years) Mean 61  63 55  47 Range 16-90 19-101 15-81 19-90 AFP value >25 ng/ml.(%) 122 (32.4) NA 350 (31.9) 1 (0.1) <25 ng/ml.(%) 163 (43.0) NA 352 (32.1) 784 (93.9) NA 93 (24.6) 387 (36.0) 51 (6.0) Stage I 175 (46.4) IA 176 (16.0) IA II 87 (23.1) IA 170 (15.5) IA III 86 (22.8) IA 572 (52.1) IA IV 6 (1.6) IA 132 (12.0) IA NA 23 (6.1) IA 46 (0.4) IA Tumor Load No Tumor Load 236 (62.6) IA 270 (24.6) LA With Tumor Load 114 (30.2) IA 825 (75.4) IA NA 27 (7.2) IA 0 IA Hepatic Postive 120 (31.8) NA 1043 (95.0) 343 (41.1) Negtive 119 (31.6) NA 10 (0.9) 483 (51.8) NA 138 (36.6) NA 45 (4.1) 9 (1.1) NA, not available; IA, inapplicable

Table 18 shows the Multivariate survival analysis for HCC patients with cp-score of methylation markers and relevant variables.

Coefficients HR CI SE z value p value Vali- Vali- Vali- Vali- Vali- Vali- Factor Training dation Training dation Training dation Training dation Training dation Training dation cp-score 0.921 0.440 2.512 1.553 1.966- 1.240- 0.125 0.115 7.360 3.836 <0.001 <0.001 3.210 1.944 AFP 0.000 0.000 1.000 1.000 1.000- 1.000- 0.000 0.000 0.936 2.229 0.349 0.026 1.000 1.000 Stage 0.498 0.832 1.646 2.298 1.157- 1.365- 0.180 0.266 2.772 3.130 0.006 0.002 2.341 3.868 Gender 0.077 0.799 0.926 2.224 0.427- 0.505- 0.395 0.757 −0.195 1.056 0.845 0.291 2.088 9.801 Age 0.015 0.011 0.985 1.011 0.962- 0.981- 0.012 0.015 1.237 0.733 0.216 0.463 1.009 1.042 HR: Hazard Ratio; CI: 95.0% confidence interval; SE: standard errors of coefficients; z value: Wald z-statistic value

TABLE 19 illustrates exemplary padlock sequences described herein. SEQ ID Marker Gene Sequence NO: cg10428836 BMPR1A ACAATATCCCAAACACATCAT  1 CATCTGTCTCTTATACACATC TCCGAGCCCACGAGACCCCCC CCCCCCCCCCCTCGTCGGCAG CGTCAGATGTGTATAAGAGAC AGNNNNNNAAATTATTTTCTT CTAATTCAAAT cg26668608 PSD ACAACCACATTCACTAAACAC  2 TGTCTCTTATACACATCTCCG AGCCCACGAGACCCCCCCCCC CCCCCCCCTCGTCGGCAGCGT CAGATGTGTATAAGAGACAGN NNNNNAAATATAAAATATTAA ACAACTAAAAA cg25754195 ARHGAP25 ACCCTCTTCTCTCACTTCCAC  3 TGTCTCTTATACACATCTCCG AGCCCACGAGACCCCCCCCCC CCCCCCCCCCCCTCGTCGGCA GCGTCAGATGTGTATAAGAGA CAGNNNNNNACATCCTAAAAA TATAAAACATA cg05205842 KLF3 ACCCCTACTACCCACCCTATC  4 TCCTGTCTCTTATACACATCT CCGAGCCCACGAGACCCCCCC CCCCCCCCCTCGTCGGCAGCG TCAGATGTGTATAAGAGACAG NNNNNNACCAACAATAAATAC ATAATAATAAT cg11606215 PLAC8 ACACAATTACCTCTCCCCTTC  5 TGTCTCTTATACACATCTCCG AGCCCACGAGACCCCCCCCCC CCCCCCCTCGTCGGCAGCGTC AGATGTGTATAAGAGACAGNN NNNNAAAAATATATTCATTCT CCAAATAAAAA cg24067911 ATXN1 TCTTTACTCTCTCAATCCAAC  6 CCTGTCTCTTATACACATCTC CGAGCCCACGAGACCCCCCCC CCCCCCCCCCCTCGTCGGCAG GCGTCAGATTGTATAAGAGAC AGNNNNNNAAATCCCAAATAC AATTTCAAAAT cg18196829 Chr 6:170 CCCTTAAACACCAATCTTCCA  7 ACCCTGTCTCTTATACACATC TCCGAGCCCACGAGACCCCCC CCCCCCCCCCCCCTCGTCGGC AGCGTCAGATGTGTATAAGAG ACAGNNNNNNAATAAAACAAA AATAACCCCAA cg23211949 Chr 6:3 ATCTTCCCAACACTCAAAACA  8 AATCCCTGTCTCTTATACACA TCTCCGAGCCCACGAGACCCC CCCCCCCTCGTCGGCAGCGTC AAGATGTGTTAAGAGACAGNN NNNNATCCCATACATTCCTAA CTCCTTTAAAT cg17213048 ATAD2 TCTTAAACCACTTCTAACTAC  9 ACCACAATCTGTCTCTTATAC ACATCTCCGAGCCCACGAGAC CCCCCCCCCTCGTCGGCAGCG TCAGATGTGTATAAGAGACAG NNNNNNAACCATTATATCCTT CCCATCTTTTT cg25459300 Chr 8:20 CTCCTCCTCTTACCACACCCT 10 TTTCCTAAACACTGTCTCTTA TACACATCTCCGAGCCCACGA GACCCCCCCCTCGTCGGCAGC GTCAGATGTGTATAAGAGACA GNNNNNNTAAATCCCCATATA TCTTTTCTTCA cg23461741 SH3PXD2A ATAAAAACACTAAAACCCTAA 11 AACACTGTCTCTTATACACAT CTCCGAGCCCACGAGACCCCC CCCCCCCCCCCCCTCGTCGGC AAGCGTCAGTGTGTATAAGAG AACAGNNNNNNATACCCTCTT TAATACAAAAA cg06482904 C11orf9 ATACAACCTAAATACAAACTT 12 CCCACTGTCTCTTATACACAT CTCCGAGCCCACGAGACCCCC CCCCCCCTCGTCGGCAGCGTC AGATGTGTATAAGAGACAGNN NNNNAAAAACCTAACCCTACC TCTAAACAAAT cg25574765 PPFIA1 CACAACCTACACTCCTCCCAA 13 CCTGTCTCTTATACACATCTC CGAGCCCACGAGACCCCCCCC CCCCCCCCCTCGTCGGCAGCG TCAGATGTGTATAAGAGACAG NNNNNNTTCCCTTACCCTAAA TATAATTAATA cg07459019 Chr 17:78 TCATTTCCCCCAACAAATTCT 14 TTTTCTTCTCCTGTCTCTTAT ACACATCTCCGAGCCCACGAG ACCCCCCCCCCCCTCGTCGGC AAGCGTCAGTGTGTATAAGAG TACAGNNNNNNACCAAAACCT TTATATTCTAA cg20490031 SERPINB5 ACATATAACTCACAACCCCTT 15 CCTACCCCTGTCTCTTATACA TCATCCCGAGCCCACGAGACC CCCCCCCCCTCGTCGGCAGCG TCAGATGTGTATAAGAGACAG NNNNNNAAAAATTTATAATAT TACTATCATCA cg01643250 NOTCH3 ACTCAAACACTCATCCACATC 16 CTGTCTCTTATACACATCTCC GAGCCCACGAGACCCCCCCCC CCCCCCCCCCCCCCCCTCGTC GGCAGCGTCAGATGTGTATAA GAGACAGNNNNNNCCCACCTA AAACTACAATC cg11397370 GRHL2 ACCAATAACAACTCACCTAAA 17 CTGTCTCTTATACACATCTCC GAGCCCACGAGACCCCCCCCC CCCCCCCCTCGTCGGCAGCGT CAGATGTGTATAAGAGACAGN NNNNNAAAACTAACCCTCAAC TCTCACAAACT cg11825899 TMEM8B CTCCCCTTAAATCAAAAACTA 18 AACATTTAAATCTGTCTCTTA TACACATCTCCGAGCCCACGA GACCCCCTCGTCGGCAGCGTC AGATGTGTATAAGAGACAGNN NNNNATTTACATTCCATCCAT CTACATTCTAT

Tumor cfDNA fraction ± STDEV Tumor Stage Status 1 0.098 ± 0.020 2 0.161 ± 0.146 3 0.301 ± 0.273 4 0.389 ± 0.178 Treatment Status Before treatment * 0.389 ± 0.148 After treatment * 0.109 ± 0.085 * Before treatment cohort included patients prior to any treatment (surgery or chemotherapy). After treatment included patients with a positive response to surgery or chemotherapy and reduction of tumor load.

Embodiment 1

recites a method of detecting the methylation status of one or more genes of a gene panel in a subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent, to generate treated DNA comprising deaminated nucleotides; and (b) detecting the methylation status in a gene selected from the gene panel consisting of BMPR1A, PSD, ARHGAP25. KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, Chromosome 8:20, SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B by (i) contacting tire treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of the gene to generate an amplified product; and (ii) analyzing the amplified product to determine the methylation status of the gene.

Embodiment 2

the method of embodiment 1, wherein the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 1-18.

Embodiment 3

the method of embodiment 1, wherein the probe is a padlock probe selected from SEQ ID NOs: 1-18.

Embodiment 4

the method of embodiment 1, wherein the gene is selected from BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20.

Embodiment 5

the method of embodiment 1, wherein the gene is selected from SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B.

Embodiment 6

the method of embodiment 1, further comprising detesting the methylations status of two or more genes from the gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, Chromosome 8:20, SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH1, GRHL2, and TMEM8B.

Embodiment 7

the method of embodiment 6, wherein the detecting comprises hybridizing under high stringency conditions with a set of probes, wherein the set of probes hybridizes up to 18 and no more than 18 genes from the gene panel.

Embodiment 8

the method of embodiment 1, further comprising determining a combined diagnostic score (cd-score) or a combined prognostic score (cp-score) based on the methylation status of the one or more genes, wherein the cd-score and the cp-score each independently correlates to an amount of circulating tumor DNA (ctDNA) present in the biological sample.

Embodiment 9

the method of embodiment 8, wherein the cd-score and the cp-score is each independently determined utilizing an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

Embodiment 10

the method of embodiment 1, wherein the biological sample is treated with a deaminating agent to generated the treated DNA.

Embodiment 11

the method of embodiment 1, wherein the subject is suspected of having hepatocellular carcinoma (HCC).

Embodiment 12

the method of embodiment 1, wherein the subject has a stage I, stage II, stage III, or stage IV HCC.

Embodiment 13

the method of embodiment 1, wherein the subject is further treated with an effective amount of a therapeutic agent.

Embodiment 14

the method of embodiment 13, wherein the treatment comprises: (a) transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy; (b) a chemotherapeutic agent or an agent for a targeted therapy; or (c) surgery.

Embodiment 15

the method of embodiment 14, wherein the chemotherapeutic agent comprises cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof.

Embodiment 16

the method of embodiment 14, wherein the agent for the targeted therapy comprises axitinib, bevacizumab, cetuximab, erlotinib, ramucirumab, regorafenib, sorafenib, sunitinib, a thymidine kinase (TK) inhibitor, or a combination thereof.

Embodiment 17

the method of embodiment 1, wherein the biological sample comprises a blood sample, a tissue biopsy sample, or circulating tumor cells.

Embodiment 18

recites a method of selecting a subject suspected of having hepatocellular carcinoma (HCC) for treatment, comprising: (a) contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20 to generate an amplified product, wherein the treated DNA is processed from a biological sample obtained from the subject; analyzing the amplified product to generate a methylation profile of the gene; (b) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC; (c) evaluating an output from the model to determine whether the subject has HCC; and (d) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC.

Embodiment 19

the method of embodiment 18, wherein the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 1-10.

Embodiment 20

the method of embodiment 18, further comprising contacting the treated DNA with at least an additional probe that, hybridizes under high stringency conditions to a target sequence of an additional gene selected from the gene panel to generated an additional amplified product, and analyze the additional amplified product to generate a methylation profile of the additional gene, thereby determining the presence of HCC in the subject.

Embodiment 21

the method of embodiment 18, wherein the contacting comprises hybridizing under high stringency conditions with a sea of probes, wherein the set of probes hybridizes up to ten and no more than ten genes from the gene panel.

Embodiment 22

the method of embodiment 18, wherein the biological sample is treated with a deaminating agent to generated the treated DNA.

Embodiment 23

the method of embodiment 18, wherein the model comprises methylation profiles of genes from the gene panel generated from an HCC positive sample.

Embodiment 24

the method of embodiment 23, wherein the HCC positive sample comprises cells from a metastatic HCC.

Embodiment 25

the method of embodiment 18 or 23, wherein the model further comprises methylation profiles of genes from the gene panel generated from a normal sample.

Embodiment 26

the method of any one of the embodiments 18 or 23-25, wherein the model Is developed based on the methylation profiles of biomarkers from Table 15 or Table 16.

Embodiment 27

the method of any one of the embodiments 18 or 23-26, wherein the model is developed using an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

Embodiment 28

the method of embodiment 18, further comprising determining the HCC as Stage I, Stage II, Stage III, or Stage IV.

Embodiment 29

recites a method selecting a subject suspected of having hepatocellular carcinoma (HCC) or lung cancer for treatment, comprising (a) contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from Tables 2, 6, 7, 9, or 10 to generate an amplified product, wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified product to generate a methylation profile of the gene; (c) applying the methylation profile to a model relating methylation profiles of genes from the gene panel to the presence to HCC or lung cancer; (d) evaluating an output from the model to determine whether the subject has HCC or lung cancer; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have HCC or lung cancer.

Embodiment 30

the method of embodiment 29, further comprising contacting the treated DNA with at least an additional probe that hybridizes under high stringency conditions to a target sequence of an additional gene selected from the gene panel to generated an additional amplified product, and analyze the additional amplified product to generate a methylation profile of the additional gene, thereby determining the presence of HCC or lung cancer in the subject.

Embodiment 31

the method of embodiment 29, wherein the probe is a padlock probe.

Embodiment 32

the method of embodiment 29, wherein the biological sample is treated with a deaminating agent to generated the treated DNA.

Embodiment 33

the method of embodiment 29, wherein the model comprises methylation profiles of genes from the gene panel generated from an HCC positive sample or a lung cancer positive sample.

Embodiment 34

the method of embodiment 33, wherein the HCC positive sample comprises cells from a metastatic HCC.

Embodiment 35

the method of embodiment 33, wherein the lung cancer positive sample comprises cells from metastatic lung cancer.

Embodiment 36

the method of any one of the embodiments 29 or 33-35, wherein the model further comprises methylation profiles of genes from the gene panel generated from a normal sample.

Embodiment 37

the method of any one of the embodiments 29 or 33-36, wherein the model is developed based on the methylation profiles of biomarkers from Tables 3, 6, 7, 9, or 10.

Embodiment 38

the method of any one of the embodiments 29 or 33-37, wherein the model is developed using an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

Embodiment 39

the method of embodiment 29, further comprising distinguishing between HCC and lung cancer.

Embodiment 40

the method of embodiment 18 or 29, wherein the treatment comprises: (a) transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy; (b) a chemotherapeutic agent or an agent for a targeted therapy; or (c) surgery.

Embodiment 41

recites a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subtext has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

Embodiment 42

the method of embodiment 41, wherein the methylation score correlates to survival for at least 6 months, 1 year, 1.5 years, 2 years, 2.5 years, 3 years, 4 years, 5 years, or more.

Embodiment 43

the method of embodiment 41, wherein the methylation score is calculated based on Cox proportional hazards (PH) regression analysis.

Embodiment 44

the method of embodiment 41, wherein the generating in step b) further comprises contacting treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B.

Embodiment 45

the method of embodiment 41, wherein the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 11-18.

Embodiment 46

the method of embodiment 41, wherein the generating comprises hybridizing under high stringency conditions with a set of probes, wherein the set of probes hybridizes up to eight and no more than eight genes from the gene panel.

Embodiment 47

recites a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC) or monitoring the progression of HCC in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from SOCS2, EPSTI1, TIA1, Chromosome 4, Chromosome 6, ZNF323, FOXP4, and GRHL2 from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) basal on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if the subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

Embodiment 48

recites a method of determining the prognosis of a subject having lung cancer or monitoring the progression of lung cancer in the subject, comprising: (a) processing a biological sample obtained from the subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) generating a methylation profile comprising one or more genes selected from NPBWR1, Chromosome 2, AAK1, SIM1, C10orf46, C17orf101, DEPDC5, ZNF323, GABRA2, PLAC8 and ADRA2B from the treated DNA; (c) obtaining a methylation score based on the methylation profile of the one or more genes; and (d) based on the methylation score, initiate a first treatment, decrease a dosage of a first therapeutic agent if the subject has experienced a remission, initiate a second treatment if die subject has experienced a relapse, or switch to a second therapeutic agent if the subject becomes refractory to the first therapeutic agent.

Embodiment 49

the method of embodiment 47 or 48, wherein the methylation score correlates to survival for at least 6 months, 1 year, 1.5 years, 2 years, 2.5 years, 3 years, 4 years, 5 years, or more.

Embodiment 50

the method of embodiment 47 or 48, wherein the methylation score is calculated based on Cox proportional hazards (PH) regression analysis.

Embodiment 51

the method of any one of the embodiments 41, 47, or 48, wherein the first treatment comprises: (a) transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy; (b) a chemotherapeutic agent or an agent for a targeted therapy; or (c) surgery.

Embodiment 52

the method of any one of the embodiments 41, 47, or 48, wherein the second treatment comprises: (a) transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy; (b) a chemotherapeutic agent or an agent for a targeted therapy; or (c) surgery.

Embodiment 53

the method of any one of the preceding embodiments, wherein the chemotherapeutic agent comprises cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof.

Embodiment 54

the method of any one of the preceding embodiments, wherein the agent for the targeted therapy comprises axitinib, bevacizumab, cetuximab, erlotinib, ramucirumab, regorafenib, sorafenib, sunitinib, a thymidine kinase (TK) inhibitor, or a combination thereof.

Embodiment 55

the method of any one of the preceding embodiments, wherein the biological sample comprises a blood sample, a tissue biopsy sample, or circulating tumor cells.

Embodiment 56

the method of any one of the preceding embodiments, wherein the subject is a human.

Embodiment 57

recites a method of diagnosing hepatocellular carcinoma (HCC) in a subject, comprising: (a) obtaining treated DNA from a blood sample from the subject; (b) contacting the treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20 to generate an amplified product; (c) analyzing the amplified product to determine a combined diagnostic score (cd-score), wherein the cd-score correlates to an amount of circulating tumor DNA (ctDNA) present in the blood sample; and (d) diagnosing the subject with HCC when the cd-score is elevated relative to a cd-score obtained from a blood sample of a heal thy subject.

Embodiment 58

the method of embodiment 57, wherein the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 1-10.

Embodiment 59

the method of embodiment 57, wherein the probe comprises about 0.100% sequence identity to a probe selected from SEQ ID NOs: 1-10 or consists of a probe selected from SEQ ID NOs: 1-10.

Embodiment 60

the method of embodiment 57, further comprising contacting the treated DNA with at least an additional probe that hybridizes under high stringency conditions to a target sequence of an additional gene selected from the gene panel to generate an additional amplified product, and analyze the additional amplified product to generate an additional cd-score.

Embodiment 61

the method of embodiment 57, wherein the contacting comprises hybridizing under high stringency conditions with a set of probes, wherein the set of probes hybridizes up to ten and no more than ten genes from the gene panel.

Embodiment 62

the method of embodiment 57, wherein the blood sample Ls treated with a deaminating agent to generated the treated DNA.

Embodiment 63

the method of embodiment 57, further comprising determining the HCC as Stage I, Stage II, Stage III, or Stage IV.

Embodiment 64

the method of embodiment 57, wherein the subject is further treated with an effective amount of a therapeutic agent.

Embodiment 65

the method of embodiment 64, wherein the treatment comprises: (a) transcatheter arterial chemoembolization, radiofrequency ablation, or brachytherapy; (b) a chemotherapeutic agent or an agent for a targeted therapy; or (c) surgery.

Embodiment 66

the method of any one of the embodiments 57-65, wherein the chemotherapeutic agent comprises cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof.

Embodiment 67

the method of any one of the embodiments 57-65, wherein the agent for the targeted therapy comprises axitinib, bevacizumab, cetuximab, erlotinib, ramucirumab, regorafenib, sorafenib, sunitinib, a thymidine kinase (TK) inhibitor, or a combination thereof.

Embodiment 68

the method of embodiment 57, wherein the cd-score is elevated by about 2-fold, 3-fold, 4 fold, 5-fold, 6-fold, 7 fold, 8 fold, 9 fold, 10 fold, or more relative to the cd-score obtained from a blood sample of a healthy subject.

Embodiment 69

the method of embodiment 57, wherein the cd-score is elevated by about 5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, or more relative to the cd-score obtained from a blood sample of a healthy subject.

Embodiment 70

the method of embodiment 57, wherein the cd-score is based on the methylation status of the one or more genes from the gene panel.

Embodiment 71

the method of embodiment 57, wherein the cd-score is determined utilizing an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

Embodiment 72

recites a method of determining the prognosis of a subject having hepatocellular carcinoma (HCC), comprising: (a) obtaining treated DNA from a blood sample from the subject; (b) contacting the treated DNA with a probe that hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of SH3PXD2A, C11orf9, PPFIA1, Chromosome 17:78, SERPINB5, NOTCH3, GRHL2, and TMEM8B to generate an amplified product; (c) analyzing the amplified product to determine a combined prognosis score (cp-score), wherein the cp-score correlates to an amount of circulating tumor DNA (ctDNA) present in the blood sample; and (d) determining the prognosis of the subject with HCC when the cp-score is elevated relative to a cp-score obtained from a blood sample of a healthy subject.

Embodiment 73

the method of embodiment 72, wherein the probe comprises about 80%, 85%, 90%, 95%, or 99% sequence identity to a probe selected from SEQ ID NOs: 11-18.

Embodiment 74

the method of embodiment 72, wherein the probe comprises about 100% sequence identity to a probe selected from SEQ ID NOs: 11-18 or consists of a probe selected from SEQ ID NOs: 11-18.

Embodiment 75

the method of embodiment 72, wherein the cp-score correlates to survival for at least 6 months, 1 year, 1.5 years, 2 years, 2.5 years, 3 years, 4 years, 5 years, or more.

Embodiment 76

the method of embodiment 72, further comprising contacting the treated DNA with at least an additional probe that, hybridizes under high stringency conditions to a target sequence of an additional gene selected from the gene panel to generate an additional amplified product, and analyze the additional amplified product to generate an additional cp-score.

Embodiment 77

the method of embodiment 72, wherein the generating comprises hybridizing under high stringency conditions with a set of probes, wherein the set of probes hybridizes up to eight and no more than eight genes from the gene panel.

Embodiment 78

the method of embodiment 72, wherein the cp-score is elevated by about 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, or more relative to the cd-score obtained front a blood sample of a healthy subject.

Embodiment 79

the method of embodiment 72, wherein the cp-score is elevated by about 5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, or more relative to the cd-score obtained from a blood sample of a healthy subject.

Embodiment 80

the method of embodiment 72, wherein the cp-score is based on the methylation status of the one ok more genes front the gene panel.

Embodiment 81

the method of embodiment 72, wherein the cp-score is determined utilizing an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.

Embodiment 82

the method of embodiment 72, wherein the subject is treated with a therapeutic agent for HCC prior to determining the prognosis of the subject.

Embodiment 83

the method of embodiment 72, wherein the subject is a naïve subject or a subject that has not been treated with a therapeutic agent for HCC.

Embodiment 84

the method of any one of the preceding embodiments, wherein the subject is a human.

While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby. 

1-30. (canceled)
 31. A method of determining a methylation profile of a subject, wherein the methylation profile comprises one or more markers, the method comprising: (a) contacting treated DNA from the subject with one or more probes to generate amplified products, wherein each probe hybridizes to the treated DNA to detect the methylation status of the one or more markers, wherein each of the one or more markers is selected from the group consisting of cg10673833, cg11225410, cg00338116, cg00552226, cg24496475, cg05414338, cg12041340, cg08343881, cg03431741, cg17126142, cg18004756, cg26205771, cg08436738, cg01604601, cg27252696, cg24917945, cg11252953, cg03550506, cg06903569, cg12865837, cg18440897, cg19255783, and cg20634573, and wherein the treated DNA is processed from a biological sample obtained from the subject to allow for determination of the methylation status of the one or more markers; and (b) analyzing the amplified products to generate the methylation status of the one or more markers, thereby determining the methylation profile of the one or more markers in the subject.
 32. The method of claim 31, wherein the methylation profile comprises a combined diagnostic score (cd-score), wherein the cd-score is based on the methylation status of the one or more markers.
 33. The method of claim 32, wherein the cd-score correlates to an amount of methylated circulating tumor DNA comprising the one or more markers present in the biological sample.
 34. The method of claim 32, wherein the cd-score is determined utilizing an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.
 35. The method of claim 31, wherein the methylation profile comprises a combined prognostic score (cp-score), wherein the cp-score is based on the methylation status of the one or more markers.
 36. The method of claim 35, wherein the cp-score correlates to an amount of methylated circulating tumor DNA comprising the one or more markers present in the biological sample.
 37. The method of claim 36, wherein the cp-score is determined utilizing an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.
 38. The method of claim 31, further comprising comparing the methylation profile of the subject to a model.
 39. The method of claim 38, wherein the model comprises a methylation profile of one or more markers from an HCC positive sample.
 40. The method of claim 39, wherein the HCC positive sample comprises cells from a metastatic HCC sample.
 41. The method of claim 38, wherein the model comprises a methylation profile of one or more markers from a lung cancer positive sample.
 42. The method of claim 41, wherein the lung cancer positive sample comprises cells from a metastatic lung cancer sample.
 43. The method of claim 38, wherein the model further comprises a methylation profile of one or more markers from a normal sample.
 44. The method of claim 38, wherein the model comprises a cd-score.
 45. The method of claim 38, wherein the model comprises a cp-score.
 46. The method of claim 31, wherein the one or more probes are ddPCR probes.
 47. The method of claim 31, wherein the treated DNA is treated with a deaminating agent.
 48. The method of claim 31, wherein the subject is a human.
 49. The method of claim 48, wherein the subject is suspected of having HCC.
 50. The method of claim 48, wherein the subject is suspected of having lung cancer.
 51. The method of any preceding claim, wherein the biological sample is a blood sample, tissue biopsy sample, or a circulating tumor cell sample. 